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Article

Capital Inflows and Working Children in Developing Countries: An Empirical Approach

by
Polyxeni Kechagia
* and
Theodore Metaxas
*
Department of Economics, University of Thessaly, 382 21 Volos, Greece
*
Authors to whom correspondence should be addressed.
Sustainability 2023, 15(7), 6240; https://doi.org/10.3390/su15076240
Submission received: 15 February 2023 / Revised: 28 March 2023 / Accepted: 3 April 2023 / Published: 5 April 2023
(This article belongs to the Section Sustainability in Geographic Science)
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Abstract

:
International capital flows and the operation of multinational enterprises (MNEs) are influenced by several socioeconomic and political factors. Among them, low labor cost is listed among the determinants that attract foreign capital, primarily foreign direct investment (FDI) inflows, which in various cases is attributed to unskilled employees, including working children. Working children, mainly in developing countries, remain an important social issue which has attracted increasing research interest, as well as the coordinated efforts of international organizations. The present research aims to empirically investigate the interaction between FDI inflows and child labor in developing countries using panel data analysis. The paper includes an extensive literature review of related empirical research on the association between child labor and FDI inflows in developing countries. The novelty of the study is attributed to its effort to empirically investigate the causality between FDI and child labor in two geographic regions that present high rates of working children, namely sub-Saharan Africa and Latin America. A sample of 42 developing countries from a period ranging from 1980 to 2019 was used and Granger causality tests were applied. The study concludes that there is a unidirectional causal relationship from FDI inflows to child labor in these regions and no causality was observed from child labor on macroeconomic independent variables. Several policies and proposals that will reduce or prevent child labor in the subsidiaries of multinational companies are included.

1. Introduction

A successful strategy to protect vulnerable groups, primarily working children, can be considered one of the top priorities in several regions. However, despite the decrease in the incidence of child labor worldwide, the problem remains unsolved and is a feature primarily in developing economies [1]. It is estimated that more than 246 million minors are involved in child labor, with 170 million of them working under hazardous conditions [2]. Child exploitation remains a universal problem, which became more severe after the migration crisis of 2015 [3]. Eradication of all forms of child labor by 2015 is listed among the Sustainable Development Goals (Target 8.7) of the United Nations [4]. Child labor is characterized as a crucial factor of the social dimension of sustainability [5] and it is included among the sustainability challenges of several agricultural sub-sectors, namely cocoa production [6,7,8], coffee production [9], etc.
International organizations have made significant efforts over the past decades in order to protect children’s rights and to prevent child labor. Specifically, the protection of children and the elimination of child labor is listed among the top priorities of the 190 countries that signed the United Nations (UN) Conventions of the Rights of the Child. Similarly, the International Labor Organization (ILO) included two Conventions aimed at the protection of minor employees and applied various programs to eliminate child labor by 2025 [10]. According to the Organisation for Economic Co-operation and Development (OECD), MNEs that operate in various countries should adhere to certain guidelines related to the protection of human rights, including child labor and health and safety [11]. Similarly, non-governmental organizations promote Corporate Social Responsibility initiatives, such as the Sustainable Agricultural Network, and aim to improve the social and environmental conditions of agriculture by applying the Critical Criterion and eliminating child labor [12].
Worldwide, as suggested by Edmonds and Pavcnik [13], working children are employed in several sectors, either by their parents or by industries and MNEs. However, the majority of the minors work mostly in agriculture, mining, fishing, manufacturing, and construction where they are exposed to various safety and health risks. Therefore, this phenomenon attracted research interest given its significant negative effect on minors’ personal and social development, as well as on the equilibrium of the market.
The present research focuses on FDI inflows, which could lead to increased income, while at the same time raise demand for a cheap labor force and cause child labor via a substitution effect. Nevertheless, despite the fact that FDI has remained a subject of study for several decades, the relationship between FDI inflows and child labor in developing countries has attracted increased research interest over the past few years [14]. Therefore, the purpose of this study is to conduct an extended literature review and to empirically investigate and discuss the impact of FDI inflows on child labor in developing economies. This study contributes to existing knowledge by undertaking an econometric analysis of the association between FDI inflows and child labor in different geographic regions for the period 1980–2019.
Section 1 briefly defines the theoretical framework of this research and presents the findings of its related literature review. Section 2 presents the empirical methodology and the data used in the analysis, and Section 3 includes the empirical results. The paper concludes with a section for suggestions and future work.

2. Theoretical Framework

2.1. The Phenomenon of Child Labor

Child labor can be attributed to myriad financial, cultural, social, and political factors. In several developing countries, it constitutes a severe public health issue attributed to the violation of children’s basic human rights. It is argued that it is a breach of these rights and includes several activities which are harmful to children [13,15].
Despite the fact that child labor occurs in both developed and developing economies, the present research focuses on the less developed ones, wherein the problem is even more intense [16]. It is observed that child labor is higher in specific regions, mostly African countries [17,18,19].
Child labor has also attracted the research of international organizations. The contributions of the ILO Convention 138 (Articles 1–3) and Convention 182 (Articles 2, 3 and 7) focused on the protection of minor employees’ rights. In particular, they aimed to set a minimum age for child labor and prevent severe forms of child labor [20,21]. In a similar manner, the UN Convention on the Rights of the Child highlighted the importance of the non-discrimination, rights, and personal development of children [22]. Similarly, the United States, Mexico, and Canada adopted the NAALC (North American Agreement on Labor Cooperation) in order to enforce the legal framework against working children in Latin America [23].
It is observed that the sub-Saharan countries present the highest prevalence of child labor (Figure 1). According to Rena and Herani [24], the sub-Saharan African countries present low school enrollment rates and a high prevalence of child labor, while Ray [25] highlighted the importance of poor school quality, focusing on the case of Ghana. Moreover, the high incidence of child labor in the region is attributed to, among other factors, high rates of poverty and high population growth [26], while Canagarajah and Nielsen [16] observed that child labor in the region is related to poverty and high school costs. In several African economies, minors engage in work activities in order to contribute to the household income, mostly in post-war countries such as in Sierra Leone [19,27]. Among the sub-Saharan African countries, Ethiopia presented a higher rate of children engaged in economic activity in 2019 (48.6%). In contrast, among all African countries, Algeria and Egypt presented child labor rates of 4.3% and 4.8%, respectively.
Apart from the sub-Saharan African countries, it is observed that several Latin American and Caribbean economies also present increased rates of child labor. As for the Latin American and the Caribbean countries (Figure 1), it is observed that Haiti and Paraguay had the highest rates of child labor in 2019 (35.5% and 17.9%, respectively), unlike Colombia, which presented a 3.6% rate of children engaged in economic activity in 2019. Child labor in Latin America is mostly attributed to poverty and limited social protections [29,30].

2.2. Causes and Consequences of Child Labor

Child labor, a crucial social issue that has attracted increased research interest which occurs mostly in developing countries, is attributed to several factors. Poverty is considered the most important factor that leads to child labor [26,31,32,33,34]. Therefore, in several developing economies, child labor is associated with low socioeconomic status, poor family well-being, and income shocks [2,35,36], as well as with the number of children in a family [37,38,39], high fertility rates [40,41], and remittances [42,43].
According to Basu [44], child labor is related to insufficient labor standards, while Jafarey and Lahiri [32] and Edmonds and Pavcnik [13] observed that the problem can also be attributed to market imperfections. In a later study, the researchers suggested that child labor is associated with limited trade activity, arguing that child labor is lower in countries that trade more [45]. Similarly, in several developing countries children are treated as potential employees, despite their legal frameworks being either inadequate or inappropriate [31]. Furthermore, it is observed that the incidence of child labor is higher in dualistic economies [1,39].
Moreover, there is vast empirical and theoretical literature devoted to the consequences of child labor, arguing that the phenomenon has severe and varied socioeconomic effects. In particular, child labor can lead to sexual exploitation, slavery, and corporal punishment [16,44]. Additionally, in several developing countries child labor is related to dualism [46], high rates of mortality [47,48], occupational injuries [49], and the victimization or economic exploitation of children [50]. Mental illnesses are also listed among the health issues experienced by working children, as observed by several studies [51,52,53].
In many cases, minor employees are exposed to poor ergonomics and chemicals, which can cause illness, disability, or even death [48]. Moreover, the phenomenon is associated with low rates of school enrollment and early dropouts [54,55,56], absenteeism, and poor academic performance [57,58]. In certain Latin American countries, such as Bolivia and Venezuela, child labor is related to poor academic performance [59]. Finally, minor employees are often malnourished, and in certain cases they are unpaid and drop out of school in order to contribute to family survival [47].
Finally, Chaudhuri and Dwibedi [60] observed that child labor is exploitative and dangerous, and is often focused on domestic help. Their study concluded that in order to eliminate child labor as domestic help, it is important to improve the welfare of poor families through direct financial assistance and redistribution of taxes. Similarly, Edmonds and Pavcnik [13] observed that child labor is related to child trafficking and even forced labor.

2.3. Previous Studies

The association between FDI inflows and child labor in developing countries has attracted research interest; however, the research has led to contrasting results. In particular, certain researchers have concluded that FDI inflows increase child labor [61,62], while others reached the conclusion that the absorption of FDI inflows reduces child labor in developing countries [63,64,65], as presented in Table 1 and Table 2, respectively.
The above-presented studies reached contrasting results despite the fact that they include large samples from developing countries, except for that of Iram and Fatima [65], who studied the case of Pakistan. Nevertheless, the researchers used different explanatory variables, time periods, and methodologies in order to investigate the impact of FDI on child labor. Additionally, Doytch, Thelen, and Mendoza [62] argued that different production sectors show contrasting effects on child labor.
The literature review demonstrates that only Sundjo et al. [68], as presented in Table 3, observed that FDI inflows do not influence child labor based on a sample of 25 sub-Saharan African economies. It is noted that Busse and Braun [69] used FDI inflows and not child labor as their dependent variable, and thus their study is excluded from the above-described tables. Moreover, Shelburne [70] studied child labor but did not include FDI inflows in his empirical model.
Therefore, previous studies reached vague and contrasting results. The present research aims to clarify the impact of FDI inflows in developing countries, studying a sample of 21 sub-Saharan African and 21 Latin American countries from 1980 to 2019. Existing knowledge proves that previous studies on FDI and working children focused on sub-Saharan Africa [26,41,68], thus this is the first paper to study the case of Latin America and the Caribbean despite the increasing rates of child labor in the region (Figure 1).
Based on the literature review’s findings, this is the first effort to empirically investigate FDI and child labor in these geographic regions, which have not been compared and investigated by previous studies, despite the fact that they present increased research interest due to high rates of child labor. Thus, in contrast to previous studies, the present research sheds light on the problem of child labor in developing countries and expands the econometric model to further investigate the role of FDI inflows and certain macroeconomic variables on child labor in these groups of countries. Finally, as presented in Table 1, this is the first empirical study to apply Granger causality tests for the investigation of the interaction between FDI and child labor in developing economies.

3. Methodology

3.1. Data and Samples

The research included secondary data collected from reliable international databases. The sources and the definitions of the used variables are presented in Table 4.
Specifically, the sample consisted of 21 developing economies in sub-Saharan Africa and 21 developing countries in Latin America and the Caribbean, as presented in Table 5 and Table 6, respectively, and the time period ranged from 1980 to 2019, based on the available data.

3.2. Methodological Approach

3.2.1. Dependent Variable

It has been argued that most low-income countries do not present reliable labor market data, let alone data on child labor [13]. According to Scanlon et al. [16], there is limited reliable data on working children. Following previous studies [63,69,71,72], the secondary school non-enrollment rates are used as a proxy for child labor, arguing that children who work are not attending school. This is based on the argument that school enrollment and child labor are incompatible activities; therefore, it is assumed that minors either work or go to school [73]. Similarly, Beegle, Dehejia, and Gatti [74] also observed that child labor is associated with dropping out of school. The use of this specific proxy overcomes the issue of missing data.
Therefore, based on the literature review and on the economic factors related to the operation of MNEs in developing countries, child labor is the dependent variable and it is modeled as a function of FDI inflows and explanatory variables. It is noted that child labor is estimated as follows:
Child labor = 100 − total secondary school enrollment
Among the studied countries in sub-Saharan Africa, as presented in Figure 2, Namibia has the highest mean secondary enrollment rate during the studied period, unlike Mozambique, which ranks last among the countries of this sub-sample. Similarly, in Latin America and the Caribbean, it is observed (Figure 3) that Brazil scores the highest mean secondary enrollment rate (% gross), while Guatemala has the lowest mean secondary enrollment rate among the countries of this region from 1980 to 2019. This could be attributed to Brazilian government policies against child labor, in particular the Bolsa Familia Program (PBF) and the Child Labor Eradication Program (PETI), which provide financial support to poor households and promote school attendance [73].

3.2.2. Explanatory Variables

The main independent variable in the model is the amount of net FDI inflows in the studied countries during 1980–2019. The literature review led to contrasting findings on the role of FDI inflows—supporters of globalization consider FDI inflows as contributing to a reduction in child labor, considering that capital inflows and child labor eradication strategies are related to an income effect wherein minors are not required to leave school and work since they have higher family incomes [63,64,75,76,77,78,79]. In contrast, other researchers suggest that FDI inflows in developing countries could increase the demand for unskilled employees, including minors, due to a substitution effect [63,64,78,79]. Therefore, as presented in Table 1, Table 2 and Table 3, the findings on the impact of FDI on child labor are vague. Figure 4 presents total net FDI inflows (% GDP) during the studied period for each country in the sample. Among the studied economies in sub-Saharan Africa, Liberia attracted more FDI compared to the rest of the countries in the sample, unlike Somalia, which absorbed less FDI than any other country. As for the Latin American and Caribbean countries (Figure 5), it is observed that Panama, Chile, and the Dominican Republic absorbed the majority of the regions’ FDI inflows (% GDP), in contrast to Ecuador and Paraguay.
Gross Domestic Product (GDP) per capita is used as a proxy for income. The literature review findings reveal a negative association between child labor and GDP per capita, as observed by Burhan, Sidek, and Ibrahim [41] in 44 African economies, Fatima [67] in 129 developing economies, Voy [66] in 82 countries, and Neumayer and de Soysa [63] in 177 countries. Similarly, Sundjo et al. [68], in a sample of 25 sub-Saharan African countries, observed a negative relationship between child labor and GDP per capita. In contrast, Iram and Fatima [65] concluded a positive but statistically insignificant relationship between the two variables. It is interesting that Dagdemir and Acaroglu [61] concluded contrasting results on the relationship between child labor and GDP per capita, represented by a U-shaped curve. According to the researchers, child labor increases in countries that present a GDP per capita higher than $7500.
Trade openness is another explanatory variable for child labor, based on the literature review. According to Edmonds and Pavnick [45], it is observed that the higher the country’s trade openness, the lower the incidence of child labor, considering that trade affects household incomes, and thus the supply of child labor. Shelburne [70] argued that trade openness provides incentives for using fewer minor employees. Similarly, Cigno, Rosati, and Guarcello [77] suggested that trade openness could either reduce or at least not affect child labor. Additionally, Jafarey and Lahiri [32] argued that trade sanctions and constraints could increase child labor, mostly in developing countries and among poor households. In contrast, other studies concluded that there is a negative effect of trade openness on child labor [65,69].
The basic model (Equation (1)), which is presented in the following section, is extended though the inclusion of additional explanatory variables, namely population and a dummy variable for the UN Convention. In particular, population is used since it is considered an important factor for child labor in developing countries based on the literature review and it is used as a proxy for country size. Sundjo et al. [68] reached to the conclusion that there is a positive relationship between child labor and rural population in 25 sub-Saharan African countries, but a negative association between child labor and population growth. Admassie [26] observed that high population growth leads to higher rates of child labor in developing economies, focusing on sub-Saharan Africa.

3.2.3. The Empirical Model

Based on the framework presented by Davies and Voy [64] and considering the model used by Dagdemir and Acaroglu [61], the present research extended their empirical models, taking into consideration the literature review findings. Specifically, based on the initial model of Davies and Voy [64] and adding a set of control variables, the model was developed as follows:
Child labor = C + β1FDIi,t + Xit + εi,t,
where X includes a variety of explanatory variables, I represents the number of countries, and t represents the annual observations.
In the present research, the additional variables used were per capita GDP and trade openness, as suggested by [61]. The dummy variable for the UN Convention is also included. Therefore, the extended model is expressed as:
Child labor = C + β1FDIi,t + β2GDPi,t + β3Populationi,t + β4Trade_opennessi,t + εi,t
Therefore, the first step is to test to causality between child labor and FDI, as well as among the explanatory variables. A Vector Autoregressive Model (VAS) was applied [80,81] and used to investigate the interrelationships among the variables, which are considered endogenous [82]. Additionally, the stationarity of the studied variables was tested and the null hypothesis was that the variable is not stationary. The Granger causality test was applied, arguing that it explains the causal influence between two variables, and, compared to other estimation techniques, it presents higher sensitivity to minor interactions [83].
Finally, based on the literature review findings, two research hypotheses were studied:
H1 
: FDI inflows are a determinant factor of child labor in the studied economies.
The majority of the empirical studies that focused on FDI and child labor in developing countries concluded that FDI inflows do play a crucial role in child labor rates, arguing that they could either positively [61,62] or negatively influence child labor [41,63,64,65,66,67]. It is therefore tested whether there is causality between the two variables.
H2 
: Macroeconomic variables in the studied economies influence child labor.
Previous studies concluded that certain macroeconomic variables affect child labor rates in developing countries. Therefore, GDP was expected to positively influence child labor [41,65,66,68,70], trade openness was expected to have a negative impact on child labor [45,65,69,70], and a positive interaction was expected between population and child labor [26,68].

4. Results

The descriptive statistics for the dependent and the independent variables are presented in Table 7. It is noted that the statistical program Eviews 11 was used.
The results of the unit root tests (ADF Test and Phillips–Peron Test) at level and first difference are presented in the Table 8. As for the lag length, the Schwarz criterion was automatically selected.
It is observed that the null hypothesis was rejected at the 1% level of significance, and therefore most of the studied variables were stationary at level. In addition, all variables were stationary at first difference. A Hausman test [84,85] was applied to choose between Fixed and Random effects, and the results are presented in Table 9:
The null hypothesis was not rejected and the Random effects model was applied. The following table (Table 10) presents panel VAR for the studied countries in Random Effects.
It is therefore concluded that there is no evidence that that GDP causes child labor in the studied countries and vice versa. Similarly, it is observed that there is no causality between child labor and population and vice versa, as well as child labor and trade openness and vice versa. The fact that the null hypothesis was not rejected in most of the above-presented cases implies that even if the studied variables are excluded, no statistical information will be lost [86]. The null hypothesis that there is no causal relationship between FDI and child labor was rejected at 5% level of significance, as well as the null hypothesis that there is no causal relationship between FDI and trade openness. Therefore, the results of the causality test demonstrate the unidirectional causal relationship between FDI and child labor and between FDI and trade openness.

5. Discussion

Child labor in developing economies is attributed to several socioeconomic factors, among which are low level of development, poverty, and poor institutional quality. The present paper focused on FDI and child labor, which remains a subject of injustice for minors, affects their present and future, and deprives them of their human and social rights. The present study concluded that there is a unidirectional causal relationship between FDI inflows in the studied economies and child labor. Previous researchers [61,62] observed a positive association between FDI and child labor, but their studies did not focus on specific geographic regions. Furthermore, the majority of the researchers concluded that there is a negative impact of FDI inflows on child labor [63,64,66,67].
Additionally, no causality was observed between GDP and child labor and vice versa. This finding is in opposition to the results of Doytch, Thelen, and Mendoza [62], who observed a negative association between GDP per capita and child labor; however, the researchers focused on different regions. Similarly, Neumayer and de Soysa [63] also concluded that there is a negative impact of GDP per capita on child labor. The negative relationship between the specific variables could be attributed to the fact that children from families with higher GDP per capita, used as a proxy for income, are less likely to leave school for economic reasons. It is thus concluded that higher GDP per capita in the studied countries contributes to a reduction in the rate of child labor.
The results of the Granger test provided no evidence of causality between trade openness and child labor and vice versa. In contrast, Iram and Fatima [65] concluded that there is a positive effect of trade openness on child labor when studying the case of Pakistan, while Neumayer and de Soysa [63] observed a negative relationship between child labor and trade openness; nevertheless, the study of Neumayer and de Soysa [63] included 117 developing economies and did not focus on a specific geographic region. Moreover, [67] observed a negative association between trade openness and child labor; however, the study included 129 developing economies from different geographic regions and argued that trade could have a negligible influence on child labor when regarding export-oriented companies. The findings of the present study are in line with [77], who argued that there is no significant impact of trade openness on child labor.
In addition, it is observed that there is no causal relationship between population and child labor in the studied regions and vice versa. However, Doytch, Thelen and Mendoza [62] reached contrasting results and argued that there is a negative association between population density and child labor. Similarly, Voy [66] also observed a negative association between population in rural areas and child labor in 82 countries.
Finally, in Table 11 the findings of the present research are systematized, taking into consideration the studied research hypotheses.
The first research hypothesis (H1) is accepted, arguing that a unidirectional causality is observed between FDI inflows and child labor in the studied groups of countries. It is, therefore, argued that FDI inflows in the countries in the sample do influence child labor rates and child labor could attract more FDI inflows. In contrast, the second research hypothesis (H2) is rejected, arguing that the macroeconomic indicators included in the econometric model do not Granger cause child labor. In other words, the macroeconomic conditions in the studied economies do not influence child labor rates, in contrast to FDI inflows.
Finally, along with the empirical findings of the present paper, we acknowledge that this research presents certain limitations. Additionally, another limitation is the diversity of child labor, considering that the phenomenon includes several types of activities. Nevertheless, the literature review findings revealed the definitions and sources of previous studies on child labor, arguing that the phenomenon refers to activities that conflict with children’s well-being. Finally, due to the limited available data, the present research investigated only net FDI inflows.

5.1. Suggestions for Future Research

Future studies could perhaps focus on the equilibrium of the labor market in case minor employees are suddenly withdrawn from multinational subsidiaries. Additionally, it would be interesting to investigate the impact of the refugee crisis on the association between child labor and FDI, arguing that unaccompanied minors are often at risk for labor trafficking. In particular, minor migrants and refugees lack adult supervision, are often exposed to harmful conditions, and are vulnerable to economic manipulation [87]. The role of additional sectors could also be studied, such as that of domestic work, manufacturing, and construction [60,88]. Moreover, future studies could even extend to the role of additional factors that could influence child labor, such as infrastructure [71,74], political stability and conflicts [60,89,90], or the role of schooling promotion [91]. There is also limited existing literature on the role of the minors’ gender in developing countries [13,38,66,92,93,94,95,96]. Thus, future researchers could focus on minor employees’ gender, considering that the interaction among FDI, child labor, and minors’ gender has not yet been investigated. Future research could also focus on certain types of FDI, namely brownfield or greenfield investment [97]

5.2. Policy Implications

Several efforts were made towards investigating child labor; nevertheless, it is argued that the extent of the problems and their consequences on working children are often underestimated [98]. Moreover, attention should be paid so that minor employees who leave the agricultural sector are not engaged in other after-school economic activities [99]. Policymakers should pay attention to domestic help provided by children, which in several cases is not mentioned despite the fact that it could also be exploitative for minor employees. Domestic help is a different dimension of child labor, which could also be a subject of future research. Additionally, policies should also consider the impact of the recent migration flows on child labor [79,100]. Local governments should collaborate with organizations that promote the minors’ well-being, such as the IREWOC (International Research on Working Children) Foundation.
Furthermore, it is argued that absenteeism and dropout rates in developing countries could be reduced by providing financial aid to poor families [58]. According to the UN Convention, every child should attend primary school for free and should be encouraged to attend higher education. It is important to consider the effect of the UN Convention since it refers to the protection of children from economic exploitation or any consequences presented by the above economic activities, such as health or mental issues [19,64].
Therefore, strengthening laws against child labor and improving working conditions could affect and ensure children’s rights. In many cases, it is difficult to investigate informal child labor. Despite the fact that child labor is illegal, it is observed that in certain African countries, it remains high in various sectors [54]. Thus, it is suggested that the legal framework should be re-considered and MNEs should collaborate with host governments in order to prevent the exploitation of children. It is also crucial to recognize victims and internment appropriately by applying a public health strategy aimed at the prevention of child exploitation or trafficking. Furthermore, the legal framework for compulsory education in the studied countries should be re-considered and motives should be provided to children and their families in order to reduce dropout rates. Regardless of the geographic region and the level of the country’s development, working children should not be treated as invisible.

6. Conclusions

The phenomenon of child labor remains an important socioeconomic problem, mainly in developing countries. The present research is the first to focus on two geographic regions that present the highest rates of child labor and investigate the causality between FDI inflows and child labor in the recipient countries of these regions. Among the studied variables, it is concluded that there is a unidirectional causal relationship between FDI inflows and child labor. In contrast, it is proven that for the studied period, there is no causality between child labor and other explanatory variables, namely GDP, trade openness, and population. However, the phenomenon has a significant impact on minors’ socioeconomic development and human rights and it is important to further expand this research and strengthen policies and frameworks against child labor.

Author Contributions

Conceptualization, P.K. and T.M.; methodology, P.K.; validation, P.K.; investigation, P.K.; resources, P.K.; data curation, P.K.; writing—original draft preparation, P.K.; writing—review and editing, T.M.; supervision, T.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are available upon request.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Prevalence of children engaged in economic activity in 2019. Source: ILOSTAT [28].
Figure 1. Prevalence of children engaged in economic activity in 2019. Source: ILOSTAT [28].
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Figure 2. Mean secondary enrollment by country in sub-Saharan Africa (1980–2019). Source: World Bank database, Authors’ calculations. The is no copyright issue.
Figure 2. Mean secondary enrollment by country in sub-Saharan Africa (1980–2019). Source: World Bank database, Authors’ calculations. The is no copyright issue.
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Figure 3. Mean secondary enrollment by country in Latin America and the Caribbean (1980–2019). Source: World Bank database, Authors’ calculations. The is no copyright issue.
Figure 3. Mean secondary enrollment by country in Latin America and the Caribbean (1980–2019). Source: World Bank database, Authors’ calculations. The is no copyright issue.
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Figure 4. Total inflows of FDI (%) GDP by country in sub-Saharan Africa (1980–2019). Source: World Bank database, Authors’ calculations. The is no copyright issue.
Figure 4. Total inflows of FDI (%) GDP by country in sub-Saharan Africa (1980–2019). Source: World Bank database, Authors’ calculations. The is no copyright issue.
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Figure 5. Total inflows of FDI (%) GDP by country in Latin America and the Caribbean (1980–2019). Source: World Bank database, Authors’ calculations. The is no copyright issue.
Figure 5. Total inflows of FDI (%) GDP by country in Latin America and the Caribbean (1980–2019). Source: World Bank database, Authors’ calculations. The is no copyright issue.
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Table
Table 1. Summary of empirical findings of studies that concluded a positive effect of FDI on child labor.
Table 1. Summary of empirical findings of studies that concluded a positive effect of FDI on child labor.
Author(s)PurposeSampleTime PeriodMethodologyDependent VariableIndependent VariableFindings
[62]To investigate the impact of disaggregated FDI inflows in different economic sectors on child labor.100 countries1990–2009Cross-country analysis
Blundell–Bond System GMM.		Child laborIncome level
FDI
Quality of institutions
Population density		The impact of FDI on child labor depends on the sector of production. A positive relationship between FDI in the agricultural sector and child labor is observed in Europe and Central Asia, while a negative one is presented in FDI in manufacturing in East and South Asia, as well as in FDI in mining in Latin American countries.
[61]To investigate the impact of globalization on child labor, focusing on the components of the process, meaning FDI and trade. 92 developing countries2000–2005OLSChild laborPer capita GDP
FDI
Trade openness
Rural area		FDI and child labor are positively related.
Child labor decreased with a certain level of FDI but increases with FDI penetration.
Table
Table 2. Summary of empirical findings of studies that concluded a negative association between FDI and child labor.
Table 2. Summary of empirical findings of studies that concluded a negative association between FDI and child labor.
Author(s)PurposeSampleTime PeriodMethodologyDependent VariableIndependent VariableFindings
[63]To empirically investigate the link between FDI and child labor.117 countries1995OLSChild laborGDP per capita
GDP
Urbanization
Agriculture
Trade openness
Stock of FDI
Public health expenditures
Public education expenditures
Pupil-to-teacher ratio		Both trade and FDI inflows are negatively associated with child labor.
[64]To investigate the extent to which child labor is affected by FDI inflows.145 countries1995Pooled regressionsChild laborFDI
GDP		There is a statistically negative association between FDI and child labor. Nevertheless, it is observed that inclusion of income level affects the impact of FDI on child labor.
[64]To investigate the association between FDI, trade openness, poverty, the value added by agriculture, the urban population, and child labor.Pakistan1970–2003Multivariate vector autoregression (VAR)Child laborFDI
Trade openness
GDP per capita
Value added by agriculture
Urban population		FDI reduces child labor.
[66]To evaluate the impact of globalization on child labor.82 countriesSurvey years vary by countryMultiple regressionChild laborFDI Population Trade openness Economic activity by gender
GDP per capita		FDI inflows are related to a lower incidence of child labor.
[41]To study the effect of income, school enrollment, fertility, and FDI on child labor in Africa.44 African countries1980–2003Panel Estimated Generalized Least Squares (EGLS)Child laborGDP per capita
Primary school enrollment Total fertility rate
FDI stock		Increasing FDI leads to a reduction in child labor in Africa.
[67]To analyze the impact of credit market imperfections and globalization on child labor.129 developing countries1970–2010OLSChild laborTrade openness
FDI inflows Real GDP per capita Domestic credit		Higher inflows of FDI lead to lower rates of child labor
Table
Table 3. Summary of empirical findings of studies that did not observe an association between FDI and child labor.
Table 3. Summary of empirical findings of studies that did not observe an association between FDI and child labor.
Author(s)PurposeSampleTime PeriodMethodologyDependent VariableIndependent VariableFindings
[68]To investigate the effect of trade and FDI on child labor in sub-Saharan Africa.25 sub-Saharan African countries1999–2013OLSChild laborTrade openness FDI inflows Rural population Primary school enrollment
GDP per capita Population growth		The impact of FDI inflows on child labor is insignificant.
Table
Table 4. Databases and definitions of the variables.
Table 4. Databases and definitions of the variables.
VariableDatabaseDefinition
Secondary school enrollment World BankGross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown.
FDIWorld BankForeign direct investment is the net inflow of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor (%GDP ).
GDPWorld BankGDP per capita is gross domestic product divided by midyear population. Data are in current U.S. dollars.
Trade opennessWorld BankRatio of total exports and imports divided by GDP.
PopulationWorld BankTotal population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.
Table
Table 5. Sub-Saharan African countries in the sample (excluding high-income countries).
Table 5. Sub-Saharan African countries in the sample (excluding high-income countries).
1Angola
2Cameroon
3Central African Republic
4Congo Dem. Rep.
5Congo Rep.
6Cote d’Ivoire
7Ethiopia
8Gabon
9Gambia
10Ghana
11Guinea
12Kenya
13Mozambique
14Namibia
15Nigeria
16Rwanda
17Senegal
18Somalia
19Uganda
20Zambia
21Zimbabwe
Table
Table 6. Latin American and Caribbean countries in the sample (excluding high-income countries).
Table 6. Latin American and Caribbean countries in the sample (excluding high-income countries).
1Argentina
2Bahamas
3Bolivia
4Brazil
5Chile
6Colombia
7Costa Rica
8Dominica
9Dominican Republic
10Ecuador
11El Salvador
12Guatemala
13Honduras
14Jamaica
15Mexico
16Nicaragua
17Panama
18Paraguay
19Peru
20Uruguay
21Venezuela
Table
Table 7. Descriptive statistics for the variables used in this research.
Table 7. Descriptive statistics for the variables used in this research.
VariableMeanMedianMaximumMinimumSt. Dev.Jarque BeraN
Secondary school enrollment 56,29855,928141,364421728.532,758900
FDI285519,15386,989−78014566705,438.9900
GDP3,680,3782070,96233,767.5096,0964,949,4798587.238900
Trade openness62,93558,626166,6986.3227,18178,491900
Population21,891,41310,288,5151.98 × 103 + 0869,65033,168,1664,716,692900
Table
Table 8. Results of the ADF and PP tests.
Table 8. Results of the ADF and PP tests.
Unit Root Tests
LnChild LaborLnFDILnGDPLnTradeLnPopulation
Panel level seriesADF
(Individual intercept)		−8820 ***
(0000)		−7115 ***
(0000)		−5179 ***
(0000)		−6027 ***
(0000)		−4178 ***
(0,0008)
ADF
(Trend and intercept)		−8821 ***
(0000)		−7132 ***
(0000)		−5221 ***
(0,0001)		−6019 ***
(0000)		−4176 **
(0005)
PP (Individual intercept)−11,643 ***
(0000)		−12,524 ***
(0000)		−5070 ***
(0000)		−5915 ***
(0000)		−4301 ***
(0,0005)
PP (Trend and intercept)−11,642 ***
(0000)		−12,536 ***
(0000)		−5113 ***
(0,0001)		−5905 ***
(0000)		−4300 ***
(0,003)
Panel first difference seriesADF
(Individual intercept)		−25,611 ***
(0000)		−20,798 ***
(0000)		−31,440 ***
(0000)		−31,238 ***
(0000)		−29,092 ***
(0000)
ADF
(Trend and intercept)		−25,596 ***
(0000)		−20,768 ***
(0000)		−31,422 ***
(0000)		−31,230 ***
(0000)		−29,075 ***
(0000)
PP (Individual intercept)−55,806 ***
(0,0001)		−53,568 ***
(0,0001)		−31,635 ***
(0000)		−31,396 ***
(0000)		−29,096 ***
(0000)
PP (Trend and intercept)−55,777 ***
(0,0001)		−53,520 ***
(0000)		−31,617 ***
(0000)		−31,392 ***
(0000)		−29,078 ***
(0000)
*** Rejection of the null hypothesis at the 1% level of significance; ** Rejection of the null hypothesis at the 5% level of significance.
Table
Table 9. Hausman test results.
Table 9. Hausman test results.
Correlated Random EffectsChi-Sq StatisticChi-Sq d.f.Prob
1223640.0157
Table
Table 10. Granger causality tests.
Table 10. Granger causality tests.
Null HypothesisF-StatProbDecision
D(LnFDI) does not Granger cause D(LnChild)41570016Reject null at 5%
D(LnChild) does not Granger cause D(LnFDI)48800078Do not reject null
D(LnGDP) does not Granger cause D(LnChild)06180538Do not reject null
D(LnChild) does not Granger cause D(LnGDP)13780252Do not reject null
D(LnPopulation) does not Granger cause D(LnChild)12730280Do not reject null
D(LnChild) does not Granger cause D(LnPopulation)00550946Do not reject null
D(LnTrade) does not Granger cause D(LnChild)12780279Do not reject null
D(LnChild) does not Granger cause D(LnTrade)27990061Do not reject null
D(LnGDP) does not Granger cause D(LnFDI)02370788Do not reject null
D(LnFDI) does not Granger cause D(LnGDP)10160362Do not reject null
D(LnPopulation) does not Granger cause D(LnFDI)03000740Do not reject null
D(LnFDI) does not Granger cause D(LnPopulation)02040814Do not reject null
D(LnTrade) does not Granger cause D(LnFDI)24260089Do not reject null
D(LnFDI) does not Granger cause D(LnTrade)73820000Reject null
D(LnPopulation) does not Granger cause D(LnGDP)25170081Do not reject null
D(LnGDP) does not Granger cause D(LnPopulation)01500860Do not reject null
D(LnTrade) does not Granger cause D(LnGDP)02670765Do not reject null
(LnGDP) does not Granger cause D(LnTrade)05330586Do not reject null
D(LnTrade) does not Granger cause D(LnPopulation)18530157Do not reject null
D(LnPopulation) does not Granger cause D(LnTrade)07180487Do not reject null
Table
Table 11. Systematization of findings.
Table 11. Systematization of findings.
Research HypothesisFindings
H1: FDI inflows are a determinant factor of child labor in the studied economiesAccepted
H2: Macroeconomic variables in the studied economies influence child laborRejected
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Kechagia, P.; Metaxas, T. Capital Inflows and Working Children in Developing Countries: An Empirical Approach. Sustainability 2023, 15, 6240. https://doi.org/10.3390/su15076240

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Kechagia P, Metaxas T. Capital Inflows and Working Children in Developing Countries: An Empirical Approach. Sustainability. 2023; 15(7):6240. https://doi.org/10.3390/su15076240

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Kechagia, Polyxeni, and Theodore Metaxas. 2023. "Capital Inflows and Working Children in Developing Countries: An Empirical Approach" Sustainability 15, no. 7: 6240. https://doi.org/10.3390/su15076240

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