Keywords

Poverty, Children's Schooling.

Introduction

Poverty is a widespread phenomenon in the world. It is characterized by a deprivation of the basic needs of human beings and a deterioration of their living conditions. It can also be characterized by an insufficiency of monetary resources to meet basic needs in terms of nutrition, education, health, etc (Abdulai & CroleRees, 2001). To make the fight against poverty a national priority, Mali has a Strategic Framework for Economic Recovery and Sustainable Development (CREDD / 2019-2023) whose general objective is to promote inclusive and sustainable development in favor of reducing poverty and inequalities in a united and peaceful Mali, based on the potential and resilience capacities in order to achieve the Sustainable Development Goals (SDGs) by 2030. This document follows several generations of strategies that have contributed to promoting sustainable development in several areas, specifically in the health and education sectors (Birdsall et al., 1986).

Investing to Improve Coverage in The Education Sector

Mali has also shown its willingness to promote education and vocational training by fighting against inequalities through the vision of the department in charge of education consisting of having an efficient and inclusive education system that trains patriotic, responsible, productive and creative citizens who contribute to the socio-economic development of their country by 2028 (PRODEC2/2019-2028).

To achieve this vision, the education system has adopted a Sector Investment Program for Education (PISE-2006), the implementation of which has had a strong impact on the supply and demand for education. The Government has thus put in place a legal and institutional framework, which has led to a relaxation of the legislation on the creation and opening of schools and has allowed private organizations, individuals, religious communities, local authorities and any other person who has available resources to create and manage educational structures according to their own principles but in compliance with the laws and regulations in force (Coulibaly et al., 2020).

Thus, in 2018-2019, the number of schools in the first cycle of basic education was 14,513 for 62,421 classrooms. During the same year, there were 4,709 schools in the second cycle of basic education for 15,604 classrooms (Dabo et al., 2015).

Despite the efforts made by the various governments to improve the school offer, disparities persist and universal schooling advocated through the Jomtien Declaration (Thailand, 1990) seems to be a mirage. Indeed, the gross enrollment rate for the first cycle is estimated at 80% for the entire country. That of boys is estimated at 86% compared to 74% for girls. As for the second cycle, the gross enrollment rate at the national level falls to 49.5% and the disparities between boys and girls remain (52.8% compared to 46.2% respectively)¹. The results of the Harmonized Survey on Household Living Conditions (EHCVM 2018) show that the incidence of poverty is higher among heads of households with no level of education (49.1%) compared to 2.8% for the higher level². "Social reproduction" and the influence of school in social stratification can be explained, from an individual point of view, by aggregation effects linked to individual behaviors (De Vreyer et al., 2012). The costs/benefits and risks of school differ with social categories, children from wealthy backgrounds have easier access to school than those from working-class backgrounds³.

The demand for education is determined by the resources that families can allocate to the schooling of their children and by the direct and indirect costs induced by the education system. Direct costs include registration fees and tuition fees charged by schools, particularly in the private sector, clothing expenses and the purchase of books or educational materials. Indirect costs are represented by the fact that when children go to school, parents must do without their labor force (Filmer, 2007). Data from the ELIM2003 survey confirm the positive link between household standard of living and school attendance since the chances of a child aged 7-18 being in school decrease substantially when household resources are eroded. Indeed, while more than half (56%) of children living in the least poor households go to school, only about a third (31%) attend school in the poorest households. EMOP 2021 data show that the percentage of primary school-aged children who are out of the system is 37.5 for the non-poor compared to 47.1 for the poor. And at the secondary education level, this rate is 47% for the non-poor compared to 57.9% for the poor (Gelli et al., 2014).

Despite visible progress in improving the provision of education services, 30 years after the Jomtien conference, the country still struggles to meet the education needs of all school-age children and to eliminate disparities in gender and living conditions. Despite the increase in school provision, why does access to school for underprivileged children remain low?. (Gewa et al., 2013)

The objective of this study is to further improve our knowledge of the obstacles related to children's access to school by supporting the State's efforts towards education for all (EFA). It is specifically about identifying the factors influencing the schooling of children from disadvantaged backgrounds through the analysis of EHCM 2021 data and making recommendations to mitigate the impact of the burdens, a source of disparities and which hinder universal access to education (Hough, 1989).

To achieve our objectives, the study is structured in three sections: a general introduction, the methodology used and a final part devoted to the analysis and discussions of the results.

Conceptual Framework

The theoretical approach to the demand for education in economics is based on the human capital model developed, where education is considered as an investment in the same way as capital. In this model considers that education is for the individual, in the same way as training, health care, migration and the search for information, a means to improve his human capital, that is to say his ability to work a potential source of improvement of his income (Kelly et al., 2004).

Data Sources

The data used come from the Harmonized Survey on Household Living Conditions (EHCVM) carried out in 2021 in the UEMOA member states. Its objective is to produce indicators for monitoring poverty and household living conditions and to provide data for the evaluation of public policies (Leslie & Pawloski, 2010).

Approach to Measuring the Determinants of Schooling of Poor Children in Mali

In this study, the target population concerned the age group of 7 to 18 years of poor households. This age group makes it possible to capture children who are eligible for basic education (I and II) while taking into account repeating classes (Michaelowa, 2001).

Explanatory Variables

Studies have shown that contextual variables, characteristics of the child, the head of household and the household positively or negatively influence the schooling of children. Through these different study results, the explanatory variables retained in the context of this work are the following: the place of residence, the region of residence, the size of the household, the sex of the head of household, the level of education of the head of household, the activity status of the CM, the age of the head of household, the marital status of the head of household, the existence of primary school 1 in the area, the major problems related to primary school 1 ; the existence of primary school 2 in the area, the major problems related to primary school 2 and individual variables (sex of the child, family relationship with the head of household, the status of the child in relation to disability, etc) (Ngnie-Teta et al., 2007).

Schooling Indicator

In this study, the variable to be explained is the “schooling status of children”. It takes the two modalities which are:

1= if children are in the education system

0= if children are outside the education system.

A child is outside the education system if he or she is out of school or has never attended formal school.

Analysis Methods

Twomethodsofanalysisareused.Descriptiveandexplanatorymethods;Thedescripti-ve analysis (Bivariate) consisted of analyzing the associations between the schooling status of the children and each of the presumed explanatory variables. These associations are measured by the frequency (in %). Their precision is measured by the “χ2” statistic on the basis of a theoretical significance threshold of 5%. The choice of its variables is made from the Multidimensional Correspondence Analysis (MCA).

The explanatory method allowed us to measure the effect of each group of variables on the dependent variable using logistic regression. This allowed us to further analyze the presumptions of observed relationships (Pierre-Louis et al., 2007).

Thus, we used in this work, simple logistic regression. Our choice focused on this method because simple logistic regression appears to be the most appropriate method for studies where the dependent variable is qualitative and dichotomous. In addition, unlike descriptive methods, it allows to explain the linear relationship between the independent variables and the dependent variable (Schemann et al., 2002).

Descriptive Analysis

The descriptive analysis (Bivariate) will consist of analyzing the associations between the schooling status of children and explanatory variables from the MCA. These associations are measured by the frequency (in %). Their precision is measured by the “χ2” statistic based on a theoretical significance threshold of 5% (Table 1).

Table 1 Schooling Status of Children and Explanatory Variables from the ACM
Explanatory variables		School attendance		Chi2
		No	Yes
Unsanitary conditions in primary school 1	No	54.66	45.34	Pearson chi2(1) = 15.1375 Pr = 0.000
	Yes	38.51	61.49
Primary school 1	No	50.63	49.37	Pearson chi2(1) = 220.2433 Pr = 0.000
	Yes	78.96	21.04
CSP of the CM	Unemployed	59.02	40.98	Pearson chi2(4) = 62.0094 Pr = 0.000
	Frame	36.33	63.67
	Worker/laborer	42.39	57.61
	Clean account work	55.18	44.82
	Other CSPs	55.7	44.3
Problem of distance from primary school 2	No	51.3	48.7	Pearson chi2(1) = 234.5868 Pr = 0.000
	Yes	76.61	23.39
Low-skilled staff in primary schools2	No	55.19	44.81	Pearson chi2(1) = 13.9618 Pr = 0.000
	Yes	35.16	64.84
Poor personal reception in primary school 2	No	53.7	46.3	Pearson chi2(1) = 16.4933 Pr = 0.000
	Yes	67.4	32.6

Unsanitary conditions in primary 1 schools may include faulty toilets, poorly maintained classrooms etc. These conditions can make the student feel uncomfortable and also affect their physical and mental health all these factors can decrease their motivation to come to school. School attendance and unsanitary conditions in the school are statistically significant at the 5% level. In other words, there is an association between school attendance and unsanitary conditions. Reading the table above tells us that among those who did not attend 38.51% believe that the problem of unsanitary conditions is an obstacle against 61.49% for those who attended whothinkthatitisnotaproblemforschoolattendance(Sidibe et al.,2022).

A basic education provides a solid foundation in key areas such as; reading, writing and mathematics. By acquiring this basic knowledge, children are better prepared to continue their studies. In this, it is found that the existence of “Fundamental School1” infrastructure has a significant effect on school attendance. Indeed, 78.96% of those who did not attend school are in an area where the fundamental school 1 infrastructure does not exist compared to 21.04% for thosewhoattended(Siri, 2014).

The socio-professional category can often determine one's chances of academic success. The analysis of this table shows us that households whose head of household is a manager or a worker have the highest attendance rate, i.e. 63.67% and 57.61% respectively, compared to 44.82% for those of self-employed workers. Indeed, individuals from more privileged social backgrounds often have access to better educational resources such as quality schools. This gives them a competitive advantage when it comes to completing their cycle. On the other hand, the less privileged may be confronted with obstacles such as absenteeism or dropping out of schoolduetotheconstraintofworkingtocontributetothefamilyincome(Thuilliez, 2010).

Among those who did not attend, 76.61% believe that the distance from school infrastructure is a challenge for schooling. On the other hand, 23.39% of those who attended school present the distance as a problem for schooling. By examining the problem of distance from school infrastructure, we can note among other things that children who have to travel long distances are more likely to be absent or arrive late. This can lead to a drop in academic resultsoradropoutamongthestudent.

Regarding the low-skilled staff, we have a rate of 35.16% among the non-schooled who think that it is a major problem. While this rate is 64.48% for the schooled. This situation is paradoxical. It suggests especially for the schooled that the lack of alternative even if the staff is not qualified is preferable to staying at home. In a case of lack of qualified human resources theyconsiderthatitisthebestoptionavailable(Thuilliez et al.,2010).

School attendance is a crucial aspect of education, however despite its importance it often happens that students encounter a poor reception from school staff. Indeed, the data shows us that for many cases of non-attendance it is due to the poor reception of staff, i.e. 67.4% against32.6%forthosewhowereabletoattend.

One of the main difficulties that students may face is access to school infrastructure. Among those who do not attend, 73.75% have difficulty accessing basic education1 compared to 26.25% for those who attend. In many rural areas where poverty is more concentrated; schools are often far away, inadequate infrastructure such as lack of classrooms can be an obstacletoschoolattendance.

A child's residential environment can have a significant influence on their schooling. Indeed, the environment plays an important role in access to school infrastructure. The proportion of children who have not attended living in a rural environment is 61.50% compared to 34.64% for the urban environment. In addition, we note that we have better school attendance in the urban environment, i.e. 65.36% compared to 38.50% for those in the rural area.

Econometric analysis

The econometric method allowed us to measure the effect of each group of variables " all things being equal " on the dependent variable using logistic regression (Table 2).

Table 2 Relationship between Schooling and the Child's Area of Residence
Variables	Coef	Odds ratio
Regions
Kayes	Ref	Ref
Koulikoro	0.211	1,235
Sikasso	-0.0797	0.923
Segou	-0.0344	0.966
Mopti	-0.590***	0.554***
Timbuktu	-0.279	0.757
Gao	-0.128	0.88
Kidal	-1,624***	0.197***
Bamako	0.574***	1,775***
Taoudeni	-2,526***	0.0799***
Menaka	-1,100***	0.333***
Place of residence
Urban	Ref	Ref
Rural	-0.544***	0.580***
***p<0.01, **p<0.05, *p<0.1

The results show a relationship between the schooling of poor children and the region variable. The analysis shows that poor children in the regions of Mopti; Kidal; Taoudéni and Ménaka are less likely to be in school compared to those in the Kayes region. On the other hand, it is found that poor children in the Bamako District are more likely to be in school than thoseintheKayesregion.

The residential environment variable is one of the causes of the schooling of poor children. When the child lives in a rural area, there is a 58% probability of not being schooled compared to those living in an urban area (Table 3).

Table 3 Relationship between Schooling and Child Characteristics
Variables	Coef	Odds ratio
Gender of the child
Male	Ref	Ref
Female	-0.229***	0.796***
Age of the child
[7-12 years]	Ref	Ref
13-18 years old	-0.183***	0.833***
Link with CM/Spouse
Child CM/Spouse	Ref	Ref
Grandchildren of CM/Spouse	0.13	1,139
Other relatives of CM/Spouse	-0.244**	0.783**
Not related to CM/Spouse	-0.652	0.521
Marital status of the child
Never married	Ref	Ref
Married/was married	-2,759***	0.0634***
Religions
Muslims	Ref	Ref
Christians	0.563**	1,757**
Other religions	-0.665**	0.514**
Ethnic groups
Bambara	Ref	Ref
Malinke	0.22	1,246
Fulani	-0.314***	0.731***
Songhay/tams/ara/haou	-0.161	0.851
Soninke	-0.172	0.842
Dogon	1,013***	2,754***
Senoufo/Dafing/mian/	-0.0274	0.973
Kakolo/KHASSO	-0.142	0.868
Other ethnicity	0.619*	1,858*
Other non-Malian ethnic groups	0.440*	1,553*
Disability all levels
No	Ref	Ref
Yes	-0.395	0.674
Major disability alone
No	Ref	Ref
Yes	-1,164**	0.312**
Activities during the last 12 months
Not occupied	Ref	Ref
Busy	-1,418***	0.242***
Family work	-1,349***	0.259***
***p<0.01, **p<0.05, *p<0.1

Compared to the child's gender on school attendance, the results show that girls have about80%probabilityofnotattendingschoolcomparedtoboys.

The age effect on school attendance is also verified, the 13-18 age group are less likely tobeamongthechildrenattendingschoolcomparedtothe7-12agegroup.

Regarding the family relationship with the head of household, it is noted that the other relatives of the head of household (nephews, nieces, etc.) are less likely to attend school comparedtothechildrenoftheheadofhousehold.

The results show the negative effect of child marriage on schooling. It is found that married children have a 6.34% chance of not attending school compared to those who are never married.

In this study, a link is noted between religion and the schooling of poor children. Parents of children practicing the Christian religion are 1.75 times more likely to attend school than those of parents practicing the Muslim religion. On the other hand, children from other religions (animist; no religion, etc.) are less likely to attend school compared to those of the Muslim religion.

The ethnicity variable is a factor that often affects children's school attendance. In Mali, poor children from the Peulh ethnic group are less likely to be in school than those from the Bambara ethnic group. On the other hand, poor children from the Dogon ethnic group, other Malian ethnic groups and others, are more likely to attend school than those from the Bambara ethnicgroup.

Poor children with disabilities face schooling problems. They have a 31% chance of not beinginschoolcomparedtochildrenwithoutdisabilities.

The results show that child labor is a contributing factor in poor children not attending school. When the child is engaged in income-generating activities or not, he or she is 24% and 26% less likely to attend school, respectively, compared to those who do nothing at home (Table 4).

Table 4 Relationship between Schooling and Characteristics of the Head of Household
Variables	Coef	Odds ratio
Gender of head of household
Male	Ref	Ref
Female	0.413**	1,512**
Education Level CM
Without level	Ref	Ref
Basic level	0.518***	1,678***
Secondary and above	1,676***	5,342***
Professional Social Framework
Unemployed	Ref	Ref
Frame	0.584***	1,793***
Worker/laborer	-0.206	0.814
Self-employment	-0.0348	0.966
Other CSPs	-0.0401	0.961
Household Size
[1 – 4 people]	Ref	Ref
5-9 peopleZ	0.179	1,196
10-14 people	0.328	1,388
More than 14 people	0.222	1,249
CM age
Under 40	Ref	Ref
40-49 years old	0.0739	1,077
50-59 years old	-0.0458	0.955
Over 59 years old	-0.0709	0.932
Marital status
Bachelor	Ref	Ref
Married	0.377	1,458
Widowed/Divorced/…)	-0.126	0.882
***p<0.01, **p<0.05, *p<0.1

Regarding the characteristics of the head of household on the school attendance of poor children, the results show a relationship between the characteristics of gender, level of education and professional social frameworks and the schooling of poor children. When the household is headed by a woman, children are more than 1.5 times more likely to be in school than if the household is under the guardianship of a man. In addition, when the head of the household has a level of education, children have a high chance of attending school compared to the head without a level of education. It is also noted that the children of heads of households who are executives are 1.80 times more likely to attend school compared to heads of households withoutemployment(Table 5).

Table 5 Relationship between Schooling of Poor Children and Education Provision in the Area
Variables	Coef	Odds ratio
Primary school 1 in the Zone
Yes	Ref	Ref
No	-0.301*	0.740*
Problems in primary school 1
No problem	-0.162	0.85
Distance	-0.308	0.735
Personal inadequacy	0.0421	1,043
Low-skilled staff	0.216*	1,241*
Bad reception	-0.615*	0.541*
Unsanitary conditions	0.392	1.48
Access difficulties	-0.629***	0.533***
Poor infrastructure	-0.244**	0.784**
Dear service	0.0988	1,104
Corruption	2,139**	8,491**
Primary school 2 in the Zone
Yes	Ref	Ref
No	0.291**	1,337**
Problems in primary school 2
No problem	-0.0388	0.962
Distance	-0.668***	0.513***
Personal inadequacy	-0.0553	0.946
Low-skilled staff	0.416**	1,516**
Bad reception	-1,017***	0.362***
Unsanitary places	0.542	1,719
Access difficulties	-0.191*	0.826*
Bad condition	-0.198	0.821
Expensiveness	-0.208*	0.812*
Corruption	0.441	1,554
Constant	0.655	1,926
Observations	5,743	5,743
***p<0.01, **p<0.05, *p<0.1

The existence of school infrastructure and its quality contribute to increasing the school attendance rate. The results of the study show that when primary school 1 does not exist in an area, children have a 74% probability of not being enrolled in school. It is noted that despite the problems of teacher qualification and corruption in primary schools 1, the schooling of children in these areas is not affected. On the other hand, the problems reported such as the poor condition and difficulties of access to primary school 1 in the areas contribute to reducing the attendanceofpoorchildren.

Compared to the primary school 2, its non-existence in the area and poorly qualified staff do not contribute to the reduction of school attendance of children. On the other hand, when it is far from the area; the poor reception of teachers; the difficulties of access and the high cost are factors which reduce the attendance of poor children.

Discussions

In this study, it is noted that there is a low level of schooling among poor children in the Central and Northern regions compared to the Kayes region. This situation can be explained by the multidimensional crisis that Mali has been experiencing since 2012. These regions are facing security problems that affect the schooling of children. In addition, the population of northern Mali has a nomadic culture, which can also impact the schooling of children.

The results on the residential environment cannot be explained by the state of the school offer between urban and rural areas. Children in rural areas do not have access to school infrastructure and often these infrastructures lack qualified personnel. All these factors can affect the schooling of children in rural areas. This result confirms the work of Kobiane in Burkina Faso, it highlights strong disparities between the administrative units that make up the country. While the province of Komondjari, an exclusively rural province, has a gross schooling rate of 15%, the province of Kadiogo which houses the capital of the country had a gross schooling rate of 90% in 2000. And in the case of Cameroon, it establishes that children in urbanareasaremorelikelytoattendschoolthanthoseinruralareas.

In view of the results, it is noted that there is a low enrollment of girls compared to boys in Mali. The low enrollment of girls compared to boys in sub-Saharan Africa is a well-known problem. In some circles, they prefer to educate boys rather than girls. They simply think that the education of boys is more beneficial to the family than that of girls because girls are led to leave the parental family. The results of the study in Burkina-Faso contradict this where schoolingisnotassociatedwiththesexofthechildren.

The analysis of children's schooling and the relationship with the head of household shows a disparity between the children of the head of household and other children related to the head of household. This situation raises the question of entrusting children to our parents. The study by Pilon, shows that entrusted children have lower schooling rates than the children of the head of household, even if there are exceptions, particularly in rural areas.

Child marriage, denounced by women's organizations, is one of the reasons why poor children in Mali do not attend school. Poverty is one of the main causes of early marriage. When poverty is great, a young girl can represent a burden, and her marriage to a much older man (sometimes an old man), a widespread practice in some societies, is a family survival strategy sometimes even considered advantageous for the young girl. In traditional societies of sub-Saharan Africa, it happens that the family of the future bride receives cattle from the groom or fromhisparentsasthepriceoftheirdaughterRwezaura.

Religion is often seen as a threat to the development of formal education. In Mali, the results of the study show that poor children from the Christian religion are more likely to attend school compared to those from the Muslim religion. This result confirms on Côte d'Ivoire, it shows that belonging to the Muslim religion and to a lesser extent to the Protestant religion, is less favorable to the school attendance of children in Abidjan than belonging to the Catholic religion. Formal schooling is often frowned upon by certain communities, especially in rural areas. They think that it is a way of conveying Western values inourcommunities.

The explanatory factors of the schooling of children can also be found in the way in which ethnic groups perceive formal education. It is noted in this study, a low schooling of the Peuhl ethnic group compared to the Bambara ethnic group. In general, the Peuhls prefer to send children to Koranic schools to learn the Muslim religion rather than to classical schools. In addition, they would find that school prevented children from taking care of the herd and broughtlittlecompensationinreturn.

Although education is compulsory in Mali and the ratification of the Convention on the Rights of Persons with Disabilities (CRPD), as well as the Optional Protocol to the CRPD, on May 15, 2007. The CRPD and the Protocol were ratified on April 7, 2008. The State of Mali, as part of the effective implementation of the various ratified conventions, adopted and promulgated Law No. 2018-027 of June 12, 2018 relating to the rights of persons living with disabilities. There is a low probability of schooling for poor children with disabilities compared to those without disabilities. Children with disabilities, particularly motor, visual, hearing or cognitive disabilities, are particularly vulnerable to exclusion. The vast majority do not attend school or drop out early. Often, these barriers to education overlap and are cumulative. In Mozambique, for example, nearly half of men with disabilities can read and write, compared to 17% of women with disabilities (UNESCO, GEM Report, op. cit). Serious attention is needed to “ensure learning opportunities for all” (Sustainable Development Goal 4 or SDG 4). In addition to these gaps, there is also a lack of research on what works in inclusive education in Mali.

School activity does not exclude the child from the world of work, while the world of work can exclude the child from school (Fukui, 1996). In the analysis of the results of our study, we observe a negative effect of economic or non-economic activities carried out by poor children on school attendance. Poverty is the primary cause of child labor and education contributes to it. Schooling is therefore the surest way to combat this scourge that continues to destroy the lives of several million children around the world. Indeed, school alone can allow a child to acquire social skills and other abilities (mainly literacy) that will be useful to him in his futurelifeandimprovehissituation:havingagoodjob,agoodsalary,etc.

In the literature, several authors have confirmed that when the household is headed by a woman, the probability of children attending school is higher than that of men. This result is confirmed in our study for the case of poor households in Mali. Lloyd show that women heads of household generally allocate a significant portion of the family budget to children than men do to the care and emotional support provided to children. I shows that it is in poor households thatwomenhaveahigherpropensitytoeducatetheirchildrenthanmen.

Generally, the higher the individual's level of education, the more open he or she is to modernity and formal education. The results of our study confirm this relationship between the level of education of the head of household and the schooling of his or her children. Several studies have shown the importance of the level of education of the head of household on the schooling of children; found that the higher the level of education of the head of household, the better the children aged 6-11 are schooled. They go further and find that manyofthem(especiallythoseaged10-14)lastintheeducationsystem.

The employment situation of the head of household affects the schooling of children. In Mali, poor children of managerial heads of household have a high probability of schooling comparedtothoseofunemployedheadsofhousehold.

The availability and quality of education provision are factors in the schooling of poor children in Mali. It is found that the distance and quality of school infrastructure are obstacles totheschoolingofpoorchildreninMali.

Lloyd showed that the availability of infrastructure explains the discrimination in access to education between rural and urban areas and is also valid for gender inequalities. The proximity and equipment of school infrastructure play a role in explaining gender inequalities in schooling. Indeed, parents are reluctant to send girls to schools that are poorly equipped or far from residential areas for fear of exposing them to all kinds of aggression.

In the case of the Maasai tribes of Tanzania states that "the weakness of the school offer, the quasi-non-existence of the transition to secondary school are all conditions which limit the educational strategies of the parents. For the fathers, the universe of possibilities is therefore very restricted. We can only really speak of educational choices for two criteria: the sex of the children in school and their number".

Conclusion and Recommendations

The objective of this study is to further improve our knowledge of the obstacles linked to children's access to school by supporting the State's efforts towards education for all (EFA). It will specifically involve identifying the factors influencing the schooling of children from disadvantaged backgrounds through the analysis of EHCM 2021 data and making recommendations to mitigate the impact of the constraints, which are a source of disparities and whichhinderuniversalaccesstoeducation.

At the end of this study, a disparity in schooling is noted between the regions of Mali. Poor children in the Central and Northern regions (Mopti; Ménaka; Taoudéni and Kidal) have a low probability of schooling compared to those in the Kayes region. In addition, the disparity exists between rural and urban areas where children in urban areas have a higher chance of attendingschoolthanthoseinruralareas.

In relation to the characteristics of poor children and schooling, the analysis shows an inequality in schooling between girls and boys. Girls are less likely to attend school than boys. Discrimination is also observed between the head of household's own children and other children in the household. Other children in the household have a low probability of schooling comparedtothechildrenoftheheadofhousehold.

We note in the study that early marriage, child labour and children with disabilities are contributingfactorstopoorchildrennotattendingschool.

Regarding the characteristics of the head of household, it is revealed that when the household is headed by a woman, children are more likely to be educated. In addition, the level of education of the head of household contributes to the schooling of children when it is high.

The availability and quality of education provision helps improve school attendance by poorchildren.

Through these different results, the strategies proposed to improve the schooling of poor children in Mali are:

• Strengthening the security of property and people in the Central and Northern regions to facilitate access to school for children;

• Availability of school infrastructure in rural areas;

• Raising awareness among parents of the benefits of educating girls;

• Make provisions for the abandonment of early marriage and child labour;

• The application of Law No. 2018-027 of June 12, 2018 relating to the rights of persons living with a disability (see Article 11)

• Making women's empowerment effective.

Endnotes

¹Monitoring Report of the Indicators of the Education System from 2017-2018 to 2021-2022, May 2022.

²Profile and determinants of poverty in Mali, 2018-2019 P-34.

³Philipe Hugon: Schooling and education: factors of growth and catalysts of development P-21.

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Received: 01-May-2025, Manuscript No. jeeer-25-15957; Editor assigned: 05-May-2025, PreQC No. jeeer-25-15957(PQ); Reviewed: 17-May-2025, QC No. jeeer-25-15957; Revised: 24-May-2025, Manuscript No. jeeer-25-15957(R); Published: 31-May-2025