essay on school dropouts in india

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Determinants of School dropouts among adolescents: Evidence from a longitudinal study in India

Pradeep kumar, sangram kishor patel, solomon debbarma, niranjan saggurti.

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Competing Interests: The authors have declared that no competing interests exist.

* E-mail: [email protected]

Received 2022 May 12; Accepted 2023 Feb 15; Collection date 2023.

This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Introduction

India has the largest adolescent population in the world. However, many unprivileged Indian adolescents are still unable to complete schooling. Hence, there is a need to understand the reasons for school dropout among this population. The present study is an attempt to understand the determinants of school dropout among adolescents and identify the factors and reasons that contribute to it.

Material and methods

Longitudinal survey data- Understanding Adults and Young Adolescents (UDAYA) for Bihar and Uttar Pradesh has been used to identify the determinants of school dropout among adolescents aged 10–19. The first wave of the survey was conducted in 2015–2016, and the follow-up survey in 2018–2019. Descriptive statistics along with bivariate and multivariate analysis was used to observe school dropout rates and factors associated with it among adolescents.

Results show that the school dropout rate was highest among married girls aged 15–19 years (84%), followed by unmarried girls (46%), and boys (38%) of the same age group. The odds of school dropout among adolescents decreased with an increase in household wealth status. School dropout was significantly less likely among adolescents whose mothers were educated as compared to mothers who had no education. Younger boys [AOR: 6.67; CI: 4.83–9.23] and girls [AOR: 2.56; CI: 1.79–3.84] who engaged in paid work were 6.67 times and 2.56 times more likely to drop out of school than those who were not. The likelihood of school dropout was 3.14 times more likely among younger boys [AOR: 3.14; CI: 2.26–4.35], and it was 89% more likely among older boys [AOR: 1.89; CI: 1.55–2.30] who consumed any substances as compared to those who did not consume any substances. Both younger [AOR: 2.05; CI: 1.37–3.05] and older girls [AOR: 1.30; CI: 1.05–1.62] who acknowledged at least one form of discriminatory practice by parents were more likely to drop out of school than their counterparts. Lack of interest in studies/education not necessary (43%) was the predominant reason among younger boys for school dropout, followed by family reasons (23%) and paid work (21%).

Conclusions

Dropout was prevalent among lower social and economic strata. Mother’s education, parental interaction, participation in sports and having role models reduce school dropout. Conversely, factors such as being engaged in paid work, substance abuse among boys, and gender discriminatory practices towards girls, are risk factors for dropout among adolescents. Lack of interest in studies and familial reasons also increase dropout. There is a need to improve the socio-economic status, delay the marital age of girls, and enhance the government incentives for education, give rightful work to girls after schooling, and provide awareness.

Education is one of the primary determining factors of development for any country [ 1 , 2 ]. It plays a significant role in enriching people’s understanding of themselves and the world. Also, education plays a crucial role in securing economic and social progress and improving income distribution [ 1 ]. No country in the world can achieve sustainable economic development without substantial investment in human capital [ 2 ]. So, considering the need and importance of the education, targets was set at the global level; in Goal 4 of the Sustainable Development Goals (SDG) framework, which talks about quality of education, and one of the targets of this goal is to ensure that all the girls and boys complete free, equitable, and quality primary and secondary education [ 3 ]. Therefore, it is essential to understand how this goal can be achieved and what progress has already been made in this regard. So, pertaining to this; the scenario at the national level as per National Education Policy (NEP) report indicates that the gross enrolment ratio (GER) for grades 6–8 was 91%, while for grades 9–10 and 11–12, it was 79% and 57%, respectively [ 4 ]. Clearly, efforts to bring children within the formal education system through primary schooling have been successful. However, the increasing dropout rates among Indian children, especially after 8th grade, has put the long-term benefits of such gross enrolment into question [ 5 ].

A longitudinal study in the US has shown that adolescent employment and school dropouts are strongly associated after adjusting for the individual- and labor-market-level factors [ 6 ]. Previous literature has also demonstrated that intensively employed students tend to be less academically successful, less engaged in school, and more likely to drop out [ 7 , 8 ]. Moreover, research in north Karnataka revealed that economic factors (household poverty; girls’ work-related migration) were associated with school dropout among adolescent girls [ 9 ]. Another author also substantiates financial obstacles as one of the reasons behind dropout [ 10 ].

Poor learning environment and bullying/harassment at school was found associated with an increased odds of school dropout among adolescent girls [ 9 ]. While, others factors like distance to school, lack of basic facilities, poor quality of education, inadequate school environment and building, overloaded classrooms, improper languages of teaching, carelessness of teachers, and security problems in girls’ schools are major causes of student dropout in different countries [ 10 ]. A cross-sectional community-based study in Raipur, Chhattisgarh, found 11% scholastic dropouts among adolescents [ 11 ]. While, poor academic performance is another determining factor [ 11 ].

Social norms and practices (child marriage; the value of girls’ education) [ 9 ], parents’ unwillingness [ 10 ], socioeconomic status, mother’s education, family violence [ 11 ] and household’s income have significant association with school dropouts [ 12 ]. In one prospective study, it was found that social relations were strongly related to the non-completion of secondary education. For example, 18-year-old girls who found family conflicts difficult to handle had a 2.6-fold increased risk of not completing secondary education. Moreover, young people from low-income families were almost three times more likely to not complete secondary education than those from high-income families [ 13 ].

Earlier literature has established a link between adolescents engaging in non-academic risky behaviors (e.g. delinquency, drug, alcohol, or cigarette use; sexual involvement, and unintended pregnancies) [ 14 – 17 ], substance abuse [ 11 ] and subsequently dropping out of high school [ 15 ]. A panel data analysis shows that children whose parents did not participate in Parent-teacher Association (PTA) meetings, discuss academic progress with school teachers, and supervise their children’s homework in the first round had a higher risk of dropout in their adolescence (round II) [ 5 ]. Poor relations with teachers and classmates at age 18 explained a substantial part of the association between income and dropouts among both girls and boys [ 13 ]. A longitudinal study found that students’ academic and behavioral engagement and achievement in 10 th grade were associated with a decreased likelihood of dropping out of school in 12 th grade [ 18 ].

Dropout can lead to several consequences as mentioned in the various studies. One of the studies mentioned that dropout from school is an issue that affects not only students who make this decision but also affects their family, the community, and society as a whole [ 19 ]. Dropping out of school also leads to under-employment and a lower quality of life for young people [ 15 , 20 ]. Globally, a large number of students drop out of school every year [ 21 , 22 ]. While, a significant number of them are found living in poverty or receiving public assistance, imprisoned, unhealthy, divorced, or single parents of children who are likely to repeat the cycle themselves [ 21 , 23 , 24 ]. Dropouts are also at a greater risk of experiencing mental health problems [ 25 ] and delinquency [ 26 ]. However, it is not clear that risky behavior negatively affects educational achievement and increases the risk of school dropout [ 27 , 28 ]. One interesting finding from earlier studies reveals that boys who dropped out of school generally worked on family farms, entered the labor market, or undertook vocational training, whereas girls tended to marry [ 29 , 30 ].

A few decades ago there was a global call to ensure ‘education for all’ under Millennium Development Goal 2, and now under SDG 4 emphasis is on quality of education; but school dropouts continue to increase in low- and middle-income countries [ 31 ]. School dropout is very common in rural India due to various underlying factors. On the other hand, India has the largest adolescent population in the world [ 32 ]. This population can benefit the country socially, politically and economically, if they are healthy, safe, educated and skillful. However, many unprivileged Indian adolescents, particularly girls, are still unable to complete schooling. Hence, there is a need to understand the reasons for school dropout among this population. There are a good number of research papers on school dropout in India, but very few focus on the adolescent population. Problems like school dropout can be a major factor in determining adolescents’ future perspectives regarding personal and social achievements. The present study is an attempt to understand the determinants of school dropout among adolescents and identify the factors and reasons that contribute to it.

Data and methods

This study utilized data from the unique longitudinal survey of adolescents aged 10–19 (Understanding the lives of adolescent and young adults study—hereafter referred to as UDAYA study/survey) in Bihar and Uttar Pradesh. The first wave of the survey was conducted in 2015–2016, and the follow-up survey in 2018–2019. A state-representative sample of unmarried boys and girls aged 10–19 and married girls aged 15–19 was collected in the 2015–16 survey. The study used a multi-stage stratified sampling design to draw sample areas for rural and urban areas separately. In each state, 150 primary sampling units (PSUs)—villages in rural areas and census wards in urban areas—were chosen as the sampling frame, based on the 2011 census list of villages and wards. Households to be interviewed were chosen by systematic sampling in each primary sampling unit (PSU). Each PSU was subjected to a comprehensive mapping and household listing operation (or in selected segments or linked villages as appropriate). The PSU was divided into two nearly equal segments based on the list; one segment was randomly chosen for conducting interviews of females, and the other for interviews of males (married girls were interviewed from both male and female segments). Detailed information about data collection, sampling design of the study has been published elsewhere [ 33 ]. The field investigators interviewed 20,594 adolescents using a structured questionnaire; the response rate for the survey was 92 percent, and 1% of selected respondents refused to participate.

In 2018–19, the study re-interviewed those who were successfully interviewed in 2015–16, and who gave consent. The UDAYA study re-interviewed 4,567 boys, and 12,251 girls out of the 20,594 respondents who were eligible. The final follow-up sample consisted of 4,428 boys and 11,864 girls, resulting in an effective follow-up rate of 74% for boys and 81% for girls. The study excluded three percent of respondents who gave inconsistent responses to questions related to age and education during the follow-up survey. The main reasons for loss-to-follow-up were that the participant had migrated (10% for boys and 6% for girls), and the participant or his/her parent or guardian refused (7% for boys and 6% for girls). We note that the characteristics of those who were re-interviewed and those who could not be re-interviewed differed significantly in terms of age, education, place of residence, caste, and religion (see Table 1 in S1 Appendix for attrition bias). The analysis presented in this paper drew on data from the subset of adolescents. The present study considered the sample of adolescents who were enrolled in school at wave 1. The sample size for boys was 3676 and 6178 for girls.

Outcome variable

School dropout was the outcome variable of this study. It was defined as a binary variable (yes/no)—whether adolescents dropped out of school between wave-1 and 2. Data pertaining to school dropout was obtained from binary indicators of the school enrolment status collected during both waves of UDAYA. The study included only those adolescents who were enrolled/correspondence in a school during wave 1 [ 5 ]. Adolescents who were enrolled in school during wave-1 but not during wave-2 were classified as “yes” (school dropout), while those who were enrolled in both waves were classified as “no” [ 5 ].

Exposure variables

The explanatory variables included in this study were: place of residence, caste, religion, wealth index, mother’s education, engaged in paid work, substance use, state, role model, parental interaction, participation in sports activities, and gender discriminatory practices at home. Place of residence was classified as urban and rural. Caste was categorized as scheduled caste/tribe, other backward class, and others. Religion was grouped into two categories: Hindu and non-Hindu. Household wealth index was constructed based on selected durable goods and amenities with possible scores ranging from 0–57; households were then divided into quintiles, with the first quintile representing households of the poorest wealth status and the fifth quintile representing households with the wealthiest status [ 34 ]. Mother’s education was coded as ‘no education’ and ‘educated’. Work status (paid work in last one year) was coded as no and yes. Substance use included consumption of tobacco products, alcohol, and drugs; if the respondent consumed any one of the products, it was coded as “yes”, otherwise “no”. The survey was conducted in two states—“Uttar Pradesh” and “Bihar”. Adolescents reported having a role model (Yes/No). The role models reported were categorized as family members/relatives, teachers, professionals, friends, army/police, sports personalities, friends, actors, politicians and others. Adolescents were considered to have parental interaction (yes/no) if they discussed any of the following topics with their mother or father in the year preceding the interview—school performance, friendship, experience of bullying, physical changes during adolescence, or how pregnancy occurs. Participation in sports activities was coded as ‘yes’ and ‘no’. The respondent was asked—“Do you play any sports or games or engage in physical activities like walking, skipping, running, yoga, etc.?” Respondents were also asked if they experienced any gender discriminatory practices at home where parents favored sons over daughters in any of the following situations—the quantity or quality of food items given, the amount of pocket money given, the type of school in which they were enrolled, and parental aspirations for the respondent’s education [ 34 ].

Statistical analysis

Descriptive statistics were used to observe the school dropout rates among adolescents. Moreover, bivariate analysis was done to find the factors associated with school dropout. A chi-square test was performed to test the significance of the association between outcome variable and predictors of school dropout. Finally, a binary logistic regression analysis was used to observe the relationship between school dropout and other explanatory variables.

The equation for logistic distribution

Where, β 0 ,….., β n , are regression coefficients indicating the relative effect of a particular explanatory variable on the outcome variable. These coefficients change as per the context in the analysis in the study.

Ethics approval and consent to participate

The study protocol was approved by the Institutional Review Board of the Population Council. We took several measures to ensure that research ethics were strictly followed. Interviews of boys and girls were undertaken in separate segments of each primary sampling unit to avoid any risk of teasing, harassment and harm to girls’ reputation if interviews of boys and girls were conducted in the same geographical segments. Interviews were conducted separately but simultaneously in cases more than one respondent was selected from a household. In order to minimise discomfort during questioning, the scenarios and terminologies described by adolescents were adapted for use in our questionnaire on sensitive topics. Based on our earlier experiences of working with young adolescents, we made the survey questions age-appropriate—for example, we did not ask about sexual and reproductive health matters with young adolescents. Interviewers underwent extensive training in ethical issues, and teams were instructed to apprise community leaders about the study and seek their support for its implementation in the community. Consent was sought from each individual to be interviewed, and for unmarried adolescents aged 10–17, consent was also sought from a parent or guardian. Names were never recorded in the computer form in which data were collected. In order to preserve the confidentiality of the respondent or the parent/guardian, signing the consent form was optional; however, the interviewer was required to sign a statement that she or he had explained the content of the consent form to the respondent or parent. Interviewers were instructed to skip to relatively non-sensitive sections in case the interview was observed by parents or other family members, call upon a fellow interviewer to conduct parallel discussion sessions with bystander, conduct interviews in locations that offered privacy for the interview and terminate interviews if privacy could not be ensured. Finally, the study team approached NGOs that conduct youth or health-related activities at the district level, help lines that work at national or sub-national levels and public health authorities and referred study participants in need of information or services.

Sample distribution of the study population ( Table 1 )

Table 1. sample distribution of the study population, 2015–16..

Note: Wave 1 refers to 2015–16

A higher proportion of adolescents lived in rural areas (81–88%), belonged to Hindu religion (82–96%), and more than half of the adolescents belonged to other backward classes (53–67%). About one-third of the adolescents’ mothers were educated (30–40%). A higher percentage of older adolescents engaged in paid work irrespective of their gender. About half of the older adolescents had a role model. A high percentage of adolescents had parental interaction and participated in sports activities.

Fig 1 shows that the overall school dropout rate was highest among married girls aged 15–19 years (84%), followed by unmarried girls (46%), and boys (38%) of the same age group.

Fig 1. School dropout among adolescent boys and girls, and married girls, 2018–19.

*overall dropouts: those who were in schooling/correspondence at wave 1 and discontinued at wave 2.

Table 2 presents bivariate association of school dropout among adolescents (different age groups and gender) and their background characteristics. Results showed that school dropout was significantly higher among older boys (39%) and girls (49%) who lived in rural areas compared to those who lived in urban areas. Caste has a significant association with adolescents’ school dropout. For instance, dropout was more prevalent among adolescents who belonged to SC/ST caste than other castes irrespective of their age and gender. Household wealth has a negative relationship with school dropout among adolescent boys and girls; the dropout was significantly higher among both adolescent boys and girls who belonged to the poorest wealth quintile and it decreases with increase of wealth status of the households. Mother’s education also has a significant association with school dropout among adolescents—it was more prevalent among both adolescent (younger and older) boys and girls whose mother had no education. Adolescents who engaged in paid work experienced higher school dropout than those who were not. The most significant difference (paid work and not in paid work) was observed among older boys (59% vs. 33%) and girls aged 15–19 years (42% vs. 21%). Similarly, both younger (41%) and older boys (51%) who consumed any substance had a significantly higher likelihood of school dropout than those who did not. Dropout among younger boys was significantly higher in Bihar (20%), however, it was higher in Uttar Pradesh among married girls (90%). Dropout was lower among adolescents who had any role model irrespective of their age and gender. Parental interaction and participation in sports among unmarried adolescents were significantly associated with school dropout–those who played sports and interacted with parents were less likely to drop out of school.

Table 2. Bivariate association of socio-economic and demographic factors with school dropouts among adolescent boys and girls, 2018–19.

Note: p-values are based on chi-square test; N/A: Not applicable

Estimates from logistic regression analysis for school dropouts among adolescent boys and girls ( Table 3 )

Table 3. estimates from binary logistic regression analysis for school dropouts among adolescent boys and girls by background characteristics, 2018–19..

@: reference category

***p<0.0001

**p<0.05

*p<0.10; AOR: adjusted odds ratio; CI: confidence interval; N/A: Not applicable

The likelihood of school dropout was significantly higher among older girls who lived in rural areas as compared to their urban counterparts [AOR: 1.30; CI: 1.12–1.50]. Moreover, the odds of school dropout were significantly higher among both boys (younger-AOR: 1.77; CI: 1.16–2.69; older-AOR: 1.54; CI: 1.16–2.05) and girls (younger-AOR: 1.78; CI: 1.20–2.64; older-AOR: 1.38; CI: 1.16–1.63) who belonged to a non-Hindu religion as compared to those who belonged to Hindu religion. The likelihood of school dropout among adolescents decreased with an increase in household wealth status. Mother’s education plays a significant role in reducing school dropout among adolescent boys and girls and married girls. School dropout was significantly less likely among adolescents whose mothers were educated as compared to mothers who had no education. Younger boys [AOR: 6.67; CI: 4.83–9.23] and girls [AOR: 2.56; CI: 1.79–3.84] who engaged in paid work were 6.67 times and 2.56 times more likely to drop out of school than those who were not. Similarly, the risk of school dropout was significantly more likely among older boys who engaged in paid work than those who were not engaged in paid work [AOR: 2.86; CI: 2.35–3.49]. The likelihood of school dropout was 3.14 times more likely among younger boys [AOR: 3.14; CI: 2.26–4.35], and it was 89% more likely among older boys [AOR: 1.89; CI: 1.55–2.30] who consumed any substances as compared to those who did not consume any substances. The odds of school dropout were 65% higher among younger boys who belonged to Bihar [AOR: 1.65; CI: 1.21–2.27]. The risk of school dropout was 22% and 13% less likely among older boys and girls, respectively, who had a role model than those who did not have. Moreover, parental interaction and participation in sports activities were significant predictors of dropout among adolescents. Both younger [AOR: 2.05; CI: 1.37–3.05] and older girls [AOR: 1.30; CI: 1.05–1.62] who acknowledged at least one form of discriminatory practice by parents were more likely to drop out of school than their counterparts.

Reasons for school dropouts among adolescent boys and girls, and married girls ( Table 4 )

Table 4. Reasons for school dropouts among adolescent boys and girls, and married girls, 2018–19.

ⴕ included got job and work for payment in cash or kind

¥ included household work, work on form/family business, care of siblings, and illness or death of a family member

£ included school too far away, no proper school facilities for boys and girls, transport not available, costs too much, not safe to send girls/boys and poor quality of teaching/education

€ included illness and not consider education/further education is necessary

₭ included, pregnancy related reason for girls and others; @: frequency less than 25; N/A: not applicable.

Lack of interest in studies/education is not necessary (43%) was the predominant reason among younger boys for school dropout, followed by family reasons (23%) and paid work (21%). Among older boys, paid work (32%) was the primary reason for school dropout, followed by lack of interest in studies/ education not necessary (29%). Among younger girls, family reasons (31%) were the main factor for school dropout, followed by school-related reasons (31%) and lack of interest in studies/ education is not necessary (26%). In contrast, school-related reasons (32%) played a significant role in school dropout among older girls, followed by family-related reasons (26%) and failures (23%). Among married girls, getting married/engaged (38%) was the major reason for school dropout, followed by failures (25%) and family-related reasons (23%).

Adolescents who lived in rural areas, belonged to SC/ST caste group, belonged to the poorest wealth quintile, and whose mother was not educated, reported more family-related reasons for school dropout compared to their counterparts irrespective of their gender and marital status. Similarly, personal reasons for school dropout were reported more by unmarried adolescents who lived in rural areas, belonged to a lower caste group, and whose mother was not educated ( Table 5 ). Moreover, paid work as a reason for school dropout was more reported by boys who lived in rural areas, who belonged to non-Hindu religions, and whose mother was uneducated compared to their counterparts ( Table 6 ).

Table 5. Reasons for dropouts among adolescents by socio-demographic and economic characteristics, 2018–19.

Table 6. reasons for dropout among adolescents by socio-demographic and economic characteristics, 2018–19..

This study examines the determinants of school dropout among adolescents in Uttar Pradesh and Bihar, based on data from the longitudinal UDAYA (Understanding the lives of adolescents and young adults) study. School dropout cannot be justified by one single reason; rather, it has several contributing factors. The main finding of this study highlights that dropout was high among married girls, and in rural areas, it was high for both boys and girls. Higher the social (Caste) and economic (Wealth quintile) strata lower the dropout rate and children from other religious background (other than Hindu) were found to have higher dropout rates in the study area. Dropout was also high among those who were engaged in paid work. Mother’s education and parental interaction were found to reduce dropout rates and the same is true with the participation in sports activities. The main reasons for dropout are ‘not interested’ in studies, family reasons, paid work and personal reasons.

At the global level, sustainable development goals have identified girl’s education as a priority, but the present study among adolescents found that school dropout rate was higher among married girls, followed by unmarried girls and younger boys. There are several possible reasons for this–an earlier study found that in Bihar, girls are married at an early age (Paul, 2021). This is further qualified with the finding that risk of dropout among girls was associated with marriage [ 35 ]. Moreover, it was found that Indian households invest equally in boys and girls at primary school level, but at secondary level of education sons are given priority above girls to study further [ 36 ]. Costs of education at secondary level are higher, which may be the factor for girls to discontinue [ 37 ].

For many health indicators, the reason for rural-urban differential is mainly due to socio-economic status of the household and parent’s education [ 38 , 39 ]. Similarly, we may attribute the higher dropout of older boys and girls in rural areas as compared to their urban counterparts to the low socio-economic status and parental education. A majority of families in rural areas are economically poor and may have food insecurity, which results in children engaging in farming and household work, thus leading to dropout [ 40 – 44 ].

Caste had a significant association with adolescent school dropout and it was prevalent more among lower social strata. This result may be substantiated by findings of UNICEF & UNESCO 2014, Prakash, Bhattacharjee, Thalinja, & Isac, 2017, wherein higher dropout rates were seen among adolescent girls of low income families living in rural areas, and belonging to a lower caste [ 9 , 45 ]. Children from different caste groups do not attend classes together, and that can lead to dropout of lower caste groups [ 46 ]. Moreover, children of scheduled caste have intrinsic disadvantages that result in less chance of going to school, even after controlling factors like wealth, parental education and motivation, and school quality, etc. [ 47 ].

Dropout was higher among adolescent boys and girls who belonged to the poorest wealth quintile—similar results have been found in other studies [ 10 , 37 , 48 ]. Furthermore, poverty interacts with other social disadvantages and pressures vulnerable children to dropout [ 49 ]. Mother’s education has a significant impact on school dropout. As found in an earlier study, children of educated parents are likely to continue schooling for longer [ 49 ]. While a mother’s educational level influences length of the girls schooling, it has also been found that illiterate parents are unable to guide their children and that results in low performance and school dropout [ 42 ].

This study found that dropout was higher among those who were engaged in paid work rather than unpaid work. As found by Agarwal, many Indian households engage in different kinds of work from an early age to support their families—girls often work as wage laborers and help their mothers in household work, and girls who engage in work frequently remain absent [ 50 ]. Time spent on paid or domestic work may leave children with less time for school and learning—as a result, paid work or domestic work leads to school dropout as found in earlier research [ 51 ].

The study found that parental interaction among unmarried adolescents plays a significant role in reducing school dropout. Parent-child interaction can help to encourage schooling and to work hard, especially among low social and economically disadvantaged families who otherwise suffer from lack of motivation and low self-esteem. Another significant finding of this study is that participation in sports activities reduced the school dropout among adolescents. This is consistent with the results of the previous literature [ 52 , 53 ]. Schools/colleges provide the platform to students for sports activities and this might be the reason for fewer dropouts among adolescents who participated in sports activities. Moreover, the present study revealed that both younger and older girls who acknowledged at least one form of favorable discriminatory practices towards boys by parents had higher chances of school dropout. Previous research also shows that gender discrimination is a major reason for school dropout along with poverty and domestic or household responsibilities [ 54 ]. The states of Bihar and Uttar Pradesh have a patriarchal value system and an earlier study shows that socio-cultural issues pertinent to gender imbalance, a patriarchal value system, and educational issues disfavored female students [ 55 ].

The present study found that engagement in paid work among adolescent boys was the major reason for school dropout. However, among girls, family-related reasons are predominant. Lack of interest in studies was another reason for dropout among adolescents. These findings are consistent with previous literature wherein multiple household duties for girls, early marriage, and poverty were the main reasons for school dropout [ 56 – 59 ]. Conversely, other studies cited financial difficulties as a reason for dropping out for both girls and boys [ 56 , 58 , 60 ].

The study has a few limitations and strengths. This study is based on two Indian states (Uttar Pradesh and Bihar), limiting the generalizability of the findings. The dropout rate may be an overestimate because of the short interval of the survey. Unmeasured factors may have biased the results. For example, information on the educational attainment of adolescents’ fathers and their occupations were not available in the study. The variable—‘parental interaction’ was constructed based on the discussion of following topics—school performance, friendship, experience of bullying, physical changes during adolescence, or how pregnancy occurs—with either the mother or father in the year preceding the interview. There is no direct question related to parental interaction on education matters or their involvement in school activities. Finally, more research is needed to understand the socioeconomic, familial, and other school-related characteristics of adolescents. Despite these limitations, this study has the strengths of a prospective design, the longitudinal nature of data, and large sample size which allows examination of a detailed picture of school dropout, and the use of multiple covariate adjustments.

In conclusion, it is found that substance use, engagement in paid work, and gender discrimination in families are the risk factors for school dropout. Conversely, factors such as higher economic status, mother’s education, having a role model, parental interaction, and participation in sports activities, are protective factors that reduce dropout among adolescent boys and girls. Girl’s schooling is a serious concern and there is a need for immediate action. This study found higher dropout rates in rural areas, specifically among girls. From this finding, one could estimate some of the underlying factors of dropout as follows—in rural areas parents are mostly illiterate and unaware about the importance of education, which results in a lack of parent-child interaction. Further, in rural areas, most households are economically poor and socially backward, so this may lead to early child marriage and pressure to engage in paid work. For boys, substance abuse is a major contributing factor towards dropout. Hence, all of these factors directly or indirectly affect dropout, and this study confirms these factors by citing existing literature.

Lastly, to reduce dropout of girls in particular, it is essential to stop child marriages and give awareness to the parents and improve socio-economic status. This can be achieved by giving rightful work to girls after their education, so that both the children and the parents will be motivated. There is also a need for gender sensitivity. The government should give proper awareness and improve girls’ incentives for education and introduce some programs that focus on the return of married girls to school.

Supporting information

Acknowledgments.

The authors are grateful to Sanjay Patnaik for his editorial support on the earlier version of this paper. The authors would also like to acknowledge the contributions of other members of the UDAYA study team at the Population Council.

Data Availability

Data were collected as part of Population Council’s UDAYA study which is publicly available on the site of Harvard Dataverse (DOI: 10.7910/DVN/RRXQNT ).

Funding Statement

The authors received no specific funding for this work.

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Decision Letter 0

Chandan kumar.

21 Nov 2022

PONE-D-22-13862Transitional School Dropouts among Adolescents: Evidence from a Longitudinal StudyPLOS ONE

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Reviewer #1: This paper uses a unique longitudinal data set to explore factors underlying school discontinuation in two states of India. Data are state representative and the two waves of the survey were conducted in 2015-16 and 2018-19. Explanatory factors are a range of socioeconomic characteristics, as well as individual characteristics reported in Wave 1, and discontinuation in the intervening period is assessed from Wave 2 data. Findings suggest that substance use, paid work participation, and gendered socialisation place adolescents at elevated risk of discontinuation, while household economic status, maternal education, as well as individual factors such as having a role model, interaction with parents, participation in sports activities have a protective effect.

While this is an interesting topic, my main concern is that currently, the paper is somewhat superficial, hypothesis are not well articulated, and findings not interpreted in sufficient depth. Evidence on five groups of adolescents (10-14, 15-19, unmarried boys and girls, married girls) is provided, but aside from pointing out that married girls are an outlier, gender and age differences suggested by determinants are hardly discussed, and the discussion disappointingly does not offer hypotheses for similarities/differences. Socioeconomic background factors (rural-urban residence, economic status, religion, caste etc are well known factors influencing schooling. But while confirming the relationship using longitudinal data is no doubt interesting, what is new and exciting about these findings is that parent-child indicators (interaction, socialisation, perhaps even mother’s education as a proxy for education) and even individual behavioural factors (substance use, having a role model, engaging in sports) are key factors influencing school discontinuation, even after confounding background factors are controlled. I would strongly recommend that the paper is recast to highlight the importance of these factors in explaining school dropout, if the same relationship emerges when other concerns described below are taken into consideration. These concerns are:

1. I found the title baffling. What is meant by “transitional” school dropouts? The paper does not explain, and I would recommend a clear title, perhaps just “Determinants of school dropout…”

2. The literature review is somewhat disjointed, it needs to be reorganised so as to synthesise what the leading correlates/determinants are, rather than just describe various articles and their conclusions.

3. The dropout indicator needs to be clearer:

a. In Table 2, the three indicators of drop out shown need to be better explained. Is an older adolescent who has completed Class 10 or Class 12 and has discontinued his/her education considered a dropout, and if so, why? Surely a cutoff of Class 10 (or 12) should not denote dropout. Perhaps this is already done, but if so, it is not described. If not done, authors need to redo their analysis, or at most, justify their use of this broad indicator.

b. Table 2 shows three measures of discontinuation – its not clear to me why overall dropout is so much greater than the other two indicators for older adolescents?

c. How is “dropout” operationalised in the multivariate analyses? Three different indicators are provided in Table 2, some clarity needed. If we assume that the minimum required level of education is Class 10 in order for adolescents to make a successful transition to adulthood, then this, or the more stringent dropout before Class 12, should be used.

4. Findings are interesting, showing that even after place of residence, religion/caste and household economic status are controlled, several reflecting parent-child relations (interaction, gendered socialisation) and individual (substance use, engaging in sports, having a role model), and confirming that both domains are important determinants of school discontinuation. However, some refinement would be helpful among the explanatory variables:

a. Parental interaction and gendered socialisation are described as dichotomous indicators, but each comprises a number of areas of interaction or gendered socialisation. Authors need to be clear – does the indicator reflect at least one of these, or does it refer to interaction on all activities probed, or gendered socialisation on all the situations discussed. Table 1 suggests that parental interaction (>80% report interaction) in particular may well be scaled or at least modified to reflect interaction on all/some items.

b. Maternal education is an important determinant of child outcomes, and it would be good, if a sufficient number of better educated women is available, to show at least three categories of education.

c. Just 1-3% of all groups aside from older boys used substances in the first survey. Does it make sense to include this indicator in the study? And what does substance mean – alcohol? Drugs? Tobacco?

5. Could authors include in the analysis any variable(s) that reflect school related obstacles to continuation, as measured in the first survey?

6. Reasons for dropout are interesting, but would authors like to reconsider the reasons clubbed under various headings. For example, “other” represents a huge chunk (19-31%), but the items shown appear to be school related reasons (no transport, cost, not safe…), why not include these as school related reasons? Likewise, education not considered necessary is quite different from illness, and should be separated.

Overall, this is an interesting paper, with new and exciting findings derived from a unique dataset. It has the potential to contribute to what is known about factors influencing premature school discontinuation in India using a far more relevant set of explanatory indicators than are typically available. However, a clear hypothesis needs to be articulated, and interpretation of findings, including gender and age similarities and differences, needs to be more thoughtful. Author may want to clarify some of the comments noted above.

Reviewer #2: The paper examines drop out decisions in India with a focus on Bihar and Uttar Pradesh, two most educationally backward states of India. To their credit, the authors also use longitudinal data. According to the manuscript, between round 1 and round 2 (or 2018-19 vs 2015–16), the UDAYA study had an effective follow-up rate of 74% for boys and 81% for girls.

So those who dropped out from school b/c of out-migration are mostly absent from the very sample that the authors used to model drop out decisions. Therefore, it is important to discuss the implications of lost to follow up from different characteristics on the drop out from school and the study findings.

Further, authors may highlight the key findings in the conclusion section for better understanding of the manuscript results. N/A need to define in the footnote of the tables.

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Author response to Decision Letter 0

Collection date 2023.

Reviewer #1: This paper uses a unique longitudinal data set to explore factors underlying school discontinuation in two states of India. Data are state representative and the two waves of the survey were conducted in 2015-16 and 2018-19. Explanatory factors are a range of socioeconomic characteristics, as well as individual characteristics reported in Wave 1, and discontinuation in the intervening period is assessed from Wave 2 data. Findings suggest that substance use, paid work participation, and gendered socialisation place adolescents at elevated risk of discontinuation, while household economic status, maternal education, as well as individual factors such as having a role model, interaction with parents, participation in sports activities have a protective effect.

Response: Thanks for the suggestion. Amendment has been done.

Response: Modification has been made as per the suggestion in revised manuscript.

Response: Here in this study, School dropout was defined as whether adolescents dropped out of school between wave-1 and 2. Adolescents who were enrolled in school during wave-1 but not during wave-2 were classified as school dropout, while those who were enrolled in both waves were classified as not dropouts. We have removed other dropouts (10 or 12) for a better understanding of the reader.

Response: We have removed other two measures of school dropouts in the revised manuscript.

Response: Authors are agree with your suggestion, however, if we take class 10 as minimum required level of successful transition then we will lose the sample of 10-14 years adolescent as they are not eligible for 10th standard. Keeping this in mind, authors defined dropouts, who were enrolled in school during wave-1 but not during wave-2.

Response: The parental interaction reflects at least one of these (interaction). The number of interactions on all the activities was very less; therefore, we chose at least one interaction on the items. Similarly, for gendered socialization, we took at least one item for the selection.

Response: The sample was not enough to make three categories of mothers’ education with five-age cohort of the adolescent therefore authors made it into two group.

Response: Substance use is important indicator or one of the reason for school dropouts. Therefore, we took it as a predictor and results show that adolescent who consumed substances had higher likelihood of school dropout. Substance use included consumption of tobacco products, alcohol, and drugs. It is mentioned in the variable description as well.

Response: Authors tried to include all possible available factors in the survey, which affect school dropout/continuation.

Response: Thanks for the suggestion. Amendment has been done in the revised manuscript.

Response: Thanks for the suggestion. This study already articulated the important findings coming from this longitudinal data. However, we again look at the findings through the gender and age differences along with covariates lenses and revised further. The hypothesis is clear to us and stated already in paper that there is differences in school drop outs by gender, age and socio-economic and behavioural characteristics. The findings are also very clearly highlighted those in the manuscript. Add further to it, these findings are also linked to the global call to ensure ‘education for all’ under millennium development goal 2, and now under SDG 4 emphasis is on quality of education. These young population can benefit the country socially, politically and economically, if they are healthy, safe, educated and skilful. However, many unprivileged Indian adolescents, particularly girls, are still unable to complete schooling. Hence, there is a need to understand the reasons for school dropout among this population. There are a good number of research papers on school dropout in India, but very few focuses on adolescent population using longitudinal data. Problems like school dropout can be a major factor in determining adolescents' future perspectives regarding personal and social achievements. The present study is an attempt to understand the determinants of school dropout among adolescents and to identify the factors and reasons that contribute to it.

Reviewer #2: The paper examines drop out decisions in India with a focus on Bihar and Uttar Pradesh, two most educationally backward states of India. To their credit, the authors also use longitudinal data. According to the manuscript, between round 1 and round 2 (or 2018-19 vs 2015–16), the UDAYA study had an effective follow-up rate of 74% for boys and 81% for girls.

Response: In UDAYA longitudinal study, in 2018-19, we interviewed again those who were successfully interviewed in 2015-16, and who consented to be re-interviewed. Of the 20,594 who were eligible for re-interview, we re-interviewed 4,567 boys and 12,251 girls. We excluded respondents (3%) who gave inconsistent response to questions related to age and education at the follow-up survey; therefore, the final follow-up sample comprised 4,428 boys and 11,864 girls, thus resulting in an effective follow-up rate of 74% for boys and 81% for girls. The main reasons for loss-to-follow-up were that the participant had migrated (10% for boys and 6% for girls), and the participant or his/her parent or guardian refused (7% for boys and 6% for girls). We note that the characteristics of those who were re-interviewed and those who could not be re-interviewed differed significantly in terms of age, education, place of residence, caste, and religion (see Appendix Table 1 for attrition bias). The analysis presented in this paper drew on data from the subset adolescents.

Appendix Table 1. The characteristics at wave 1 of adolescents who were re-interviewed and who were not

Baseline Variable Respondents lost to follow up Respondents interviewed in the follow-up sample Mean difference

Years of education (mean) 7.33 7.37 0.04

Completed 8 or more years of education (%) 58.70 58.60 0.10

Currently in School (%) 57.00 64.80 7.8***

Mothers level of education (mean) 2.91 2.51 0.40***

Place of residence (%) 45.20 57.50 12.3***

Social group (% SC\\ST) 21.60 24.30 2.7***

Religion (% Hindu) 73.70 80.00 6.3***

HH wealth Score (mean) 22.57 21.51 1.06***

Total number of respondents 4302 16292

*** p<0.01, ** p<0.05, * p<0.1

Submitted filename: Response to Reviewers.docx

Decision Letter 1

16 Feb 2023

Determinants of School Dropouts among Adolescents: Evidence from a Longitudinal Study in India

PONE-D-22-13862R1

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20 Feb 2023

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Open Access

Peer-reviewed

Research Article

Detection of hotspots of school dropouts in India: A spatial clustering approach

Roles Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Mathematics, SRM Institute of Science and Technology, Chengalpattu, Tamil Nadu, India

Roles Conceptualization, Methodology, Supervision, Writing – review & editing

Affiliation Centre for Statistics, SRM Institute of Science and Technology, Chengalpattu, Tamil Nadu, India

• Raghul Gandhi Venkatesan, 
• Bagavandas Mappillairaju

• Published: January 17, 2023
• https://doi.org/10.1371/journal.pone.0280034
• Reader Comments

School dropout is a significant concern universally. This paper investigates the incorporation of spatial dependency in estimating the topographical effect of school dropout rates in India. This study utilizes the secondary data on primary, upper primary, and secondary school dropout rates of the different districts of India available at the Unified District Information System for Education plus (UDISE+) for the year 2020 to contemplate the impact of these dropouts from one region to different regions in molding with promotion rate and repetition rate. The Global Moran’s I, Univariate and Bivariate Local Indicators of Spatial Association, and spatial models are utilized to investigate the geographical variability and to find the possible relationship between dropout rates and the school-level factors at the district level. The outcomes provide clear spatial clustering and precisely highlight the hot zone dropout regions with high repetition and low promotion rates. Based on this study’s results, educational administrators can make evidence-based decisions to reduce dropout rates in hot zones of various regions of India. Furthermore, futuristic studies focusing on linking spatial hot zones with causal factors will add consistent data in assisting policymakers in taking necessary measures to develop a sound education management system.

Citation: Venkatesan RG, Mappillairaju B (2023) Detection of hotspots of school dropouts in India: A spatial clustering approach. PLoS ONE 18(1): e0280034. https://doi.org/10.1371/journal.pone.0280034

Editor: Lalit Kumar Sharma, Zoological Survey of India, INDIA

Received: June 3, 2022; Accepted: December 20, 2022; Published: January 17, 2023

Copyright: © 2023 Venkatesan, Mappillairaju. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Education is a foundation for human progress toward creating a healthy society. Its effects are significant in the development of individuals and the whole country. India’s school education vision 2030 intends to qualitatively improve the nation’s current educational system and provide high-quality education to all children of the school-attending age group, whose numbers are estimated to climb from 25 crores in 2010 to 30 crores in 2030. Though the school enrolment rates have increased in the past few years, the percentage of students who drops out of school has either remained the same or increased. As per UDISE+ 2020 report, the dropout rate in the secondary level (17%) is still high compared to the primary (1.8%) and upper primary level (1.5%). This percentage of school dropouts adds a quantitative inclusion burden to India’s vision of establishing education goals [ 1 ].

The Dissimilarity in the primary education curriculum and school infrastructure has been noted to play a key role in primary education outcomes. As a result, the government executed a new policy named "universalization of elementary education" (UEE) in 2001. UEE focuses on three major elements: universalization, which guarantees that all students between the ages of 6 and 14 have direct exposure to a school; enrolment universalization, which guarantees that all students in the aforesaid age are enrolled in school; and retention universalization, which guarantees that students who started primary school progress till they complete the upper primary level [ 2 , 3 ].

In the continuum of the retention universalization goal, research on factors influencing school dropout generally concentrates on child, family, school and community-related factors [ 4 – 8 ]. Only a few studies incorporated the regional variation in the proportion of dropout rate [ 9 ]. The use of geographic information systems in educational research, planning, and policy strategizing is a relatively recent development. However, it is becoming more common as researchers realise the benefits of providing a visual depiction of statistical data [ 10 ]. A recent study from India used spatial techniques to identify district-level variation between education programs and literacy rates based on the first major element of UEE. School dropouts remarkably increase quantitative content to the third major element of UEE—retention universalization [ 11 ]. Universally, Jose Eos Trinidad (2022) used spatial tools to analyze the determinants of high school dropout with race and poverty in New York City [ 12 ]. Mark. J. Schafer (2006) performed school-level spatial analysis to determine the relationship between school-level factors and high school dropout in Louisiana [ 13 ]. In India, Our study is the first to examine the regional variability and school-level risk factors of primary, upper primary and secondary dropouts across all the districts of India. District-level promotion rate and repetition rate of boys and girls are the School level risk factors examined in this study for possible impact on dropout. Each district is chosen as the analytical unit and used as a spatial region to highlight the regional variations in dropout and school-level factors in contemplation to carefully explore the relationships adjusting the potential covariates resulting from geographical influences.

To improve the efficiency of the school management framework, this approach incorporating geospatial technology will assist policy-makers and researchers in better understanding the existing status. Additionally, this would support the Sarva Shiksha Abhiyan and National Education Policy in providing direction for formulating preventive measures to decrease dropouts. The spatial representations of education policies across districts can help to guarantee that every student in India completes their schooling at any cost to enhance their quality of life. Policy-makers and government representatives can also utilize findings from this study to improve the current policies, fostering India’s school education and assisting in developing, testing, and implementing cost-effective strategies in hotspot regions to reduce dropouts in India.

Material and methods

The UDISE+ of India recently released data for the year 2020, accessible on the UDISE+ website https://dashboard.udiseplus.gov.in . The reports have been published under the Department of School Education and Literacy (DoSEL), Ministry of Education, Government of India. This report is based on data that schools with active UDISE+ codes in a reference year voluntarily uploaded using a data collection format (DCF) specifically created for this report. The State/UT government of the school’s location assigns the UDISE+ code for institutions. The District Education Officer (DEO) at the district level ensures that the information entered into the DCFs is accurate. This report offers essential information on several factors, such as the number of schools, teachers, and students who were enrolled, promoted, and dropped out, in terms of counts and percentages. This data source is the input for the analysis [ 1 ].

Response and predictors

Following the conceptual frameworks of earlier studies [ 12 – 14 ], the district-level overall dropout rates for primary, upper primary, and secondary levels were the outcome variables considered in this study. Utilizing the aggregate district-level data, this study included six independent variables: promotion rate, repetition rate, and a dropout rate of boys and girls.

India digital map

The primary researcher downloaded India’s district-level base map from kaggle at https://www.kaggle.com/datasets/raghulgandhi/indian-district-map-726 . Initially, the number of districts reported in UDISE+ was 733. However, a few districts in Arunachal Pradesh, Delhi, Karnataka, Manipur, Tamil Nadu, and West Bengal were merged for analysis purposes using their boundary, and the number of districts considered for the analysis was 726 given in S1 Dataset .

A positive spatial autocorrelation suggests that spots with identical data points are strongly connected in the area, while a negative spatial autocorrelation shows that strongly connected spots are more distinct. The values of Moran’s I typically range from (-1, 1), with positive measures indicating the geographical grouping of comparable measures and negative measures indicating the spatial grouping of different measures. In the absence of any spatial autocorrelation, a measure of 0 indicates a random geographical distribution. The association of nearby values around a particular geographic region is measured by univariate LISA [ 17 ]. It establishes the degree of spatial grouping and unpredictability that the data exhibit [ 18 , 19 ].

Four different types of autocorrelations were identified based on the Moran’s scatter plots and are referred to as:

• Districts with high measures and identical neighbouring districts are known as “hot spots” (High-High).
• Districts with low measures and identical neighbouring districts are known as “cold spots” (Low-Low).
• Districts that are high in measures but have low-measure neighbouring districts (High-Low) and districts that are low in measures but have high-measure neighbouring districts (Low-High) are known as “Spatial Outliers”.

In the same way, bivariate LISA were also calculated to examine the relationship between the repetition rate and promotion rate of both boys and girls of regions with different dropout rates.

LISA cluster and significance map were produced in the Geo-Da by utilizing the LISA tools. The map shows the districts with significant Moran’s I value categorized in terms of spatial autocorrelation, where hotspots are defined by red, coldspots by deep blue, and spatial outliers by light blue and light red. To investigate the possible relationships between the dropouts and predictors, we carried out statistical regressions. We first used the ordinary least square (OLS) model, then we calculated the spatial autocorrelation in the OLS regression residuals to check the spatial heterogeneity caused by spatial dependency. As soon as we determined that the Moran’s I statistic for each of the outcomes was statistically significant, we calculated the spatial lag model (SLM) and spatial error model (SEM) to obtain unbiased measures of the correlations between the predictors and dropout while addressing the geographical heterogeneity that existed in the data. A standard SLM assumes that the data points are spatially dependent and lag to one another in the nearby regions, In contrast, the SEM assumes that the disturbance terms are correlated with nearby geographical units. The best model was then determined by comparing the Akaike Information Criterion (AIC) and Schwartz Criterion (BIC) values, and we observed that SEM provided the better fit for this particular study.

SEM, if residuals reveal spatial dependency, the subsequent model effectively manages the spatial effect.

Here, λ denotes the auto-regressive parameter; ζ is the i.i.d. disturbance term. By increasing the relevant likelihood functions, both SEM and SLM are estimated [ 13 ]. QGIS desktop 3.26.2 and GeoDa 1.20.0.8 software were used for the statistical analysis.

Table 1 gives the nation’s overall dropout rate, promotion rate, and repetition rate. Results show that the secondary dropout rate is the biggest concern than the upper primary and primary dropout rates in India. The summary of the predictors shows that 99 percent of students at the primary level, 97 percent of students at the upper primary level and 84 percent of students at the secondary level were promoted. In the case of the repetition rate, 0.51 percent of students at the primary level, 0.56 percent of students at the upper primary level and 1.85 percent of students at the secondary level repeated their grades in India.

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https://doi.org/10.1371/journal.pone.0280034.t001

Spatial pattern and clustering of school dropouts across India

The spatial distribution of districts’ primary, upper primary and secondary dropout rates is depicted in Fig 1A–1C . The color highlights the geographical variations in dropout rates. The lighter color represents a lower dropout rate, whereas the darker color represents a greater dropout rate in certain districts. From the spatial maps, it is obvious that dropout rates vary geographically throughout the districts.

(A) Primary dropout. (B) Upper primary dropout. (C) Secondary.

https://doi.org/10.1371/journal.pone.0280034.g001

The estimated values of the Moran’s I statistic shows primary, upper primary and secondary dropout rates across the districts have spatial autocorrelation. The LISA cluster map in Fig 2A and the significance map in Fig 2B show 57 hotspots in primary dropout across the Indian districts. Similarly, Fig 2C shows 53 hotspots in upper primary dropout in Indian districts. In secondary dropout, 58 hotspots were identified and are shown in Fig 2E . The number of districts in each state which are hotspots is given in Table 2 and the S1 Appendix gives Moran’s, I statistic, cluster number and p-value for all 726 districts.

(A) LISA map of primary dropout rate (I = 0.349, <0.05). (B) Significance map of primary dropout. (C) LISA map of upper primary dropout rate (I = 0.367, <0.05). (D) Significance map of upper primary dropout. (E) LISA map of secondary dropout rate (I = 0.316, <0.05). (F) Significance map of secondary dropout.

https://doi.org/10.1371/journal.pone.0280034.g002

https://doi.org/10.1371/journal.pone.0280034.t002

The estimated values of bivariate LISA shown in Table 3 indicate the results of the spatial relationship between the primary, upper primary and secondary dropout rate with the other predictors taken in this study. Among the various predictors, the promotion rate of boys and girls consistently showed a negative, dropout rate of boys and girls showed a positive spatial autocorrelation at the 5% level of significance with primary, upper primary and secondary dropout across the districts. In the case of Upper primary dropout, the repetition rate of boys and girls showed positive spatial autocorrelation at the 5% level of significance. In the case of secondary dropout, boys’ repetition rate showed a negative spatial autocorrelation at significance level (<0.05) and girls’ repetition rate showed very low (insignificant) spatial autocorrelation.

https://doi.org/10.1371/journal.pone.0280034.t003

Estimated outcomes from the OLS, SLM and SEM models

Table 4 presents findings from OLS, SLM, and SEM models that describe how factors affect different dropouts after controlling for topographical effects. Based on the model selection criteria, SEM was observed to be the better-fitted model for all primary, upper primary, and secondary dropouts.

https://doi.org/10.1371/journal.pone.0280034.t004

Primary dropout.

The estimated coefficients of OLS regression were 0.103(<0.05) for boys’ promotion rate, -0.113(<0.05) for girls’ promotion rate, 0.604(<0.05) for boys’ dropout rate, 0.418(<0.05) for girls dropout rate were statistically significant to primary dropout. After making spatial modifications using the spatial model, it was observed that the pattern of the relationship between predictors and primary dropouts remained the same. We found SEM (2415.43, 2447.55) as the best fit since it has the lowest AIC and BIC values when comparing SLM (2430.42, 2467.12) and OLS (2428.53, 2460.65).

Upper primary dropout.

The estimated coefficients of OLS regression were -0.255(0.001) for boys’ promotion rate, 0.245(0.001) for girls’ promotion rate, -0.345(0.001) for boys’ repetition rate, 0.327(0.001) for girls’ repetition rate, 0.229(0.001) for boys dropout rate, 0.785(0.001) for girls dropout rate were statistically significant to upper primary dropout. After making spatial modifications using the spatial model, it was observed that the pattern of the relationship between predictors and upper primary dropouts remained the same. The corresponding AIC and BIC values of OLS (2405.81, 2437.92), SLM (2407.64, 2444.34) and SEM (2405.63, 2437.74) are obtained and SEM is considered to be the best fit.

Secondary dropout.

The estimated coefficients of OLS regression were 0.059(0.001) for boys’ promotion rate, -0.075(0.001) for girls’ promotion rate, -0.101(0.001) for boys’ repetition rate, 0.099(0.001) for girls repetition rate, 0.624(0.001) for boys dropout rate, 0.377(0.001) for girls dropout rate were highly significant to secondary dropout. After making spatial modifications using the spatial model, it was observed that the pattern of the relationship between predictors and secondary dropouts remained the same. We found SEM (2722.45, 2754.56) as the best fit since it has the lowest AIC and BIC values when comparing OLS (2723.74, 2755.85) and SLM (2725.64, 2762.34).

School graduation denotes promotion from primary to secondary. Hence high promotion rate yields a collective benefit in aligning towards the retention universalization policy of UEE, propelling the whole education system processing towards India’s vision for 2030. Our study, based on geospatial techniques, reveals that School dropouts are one of the essential criteria for increasing a country’s overall school graduation percentage. Hence, dropping out is an issue that has to be solved [ 20 ]. Researchers have examined potential influences and identified significant effects of child, family, school, and community factors [ 21 – 25 ]. In addition to these specific concerns, the impact is even worse when child and family factors such as sex, caste, and religion combine with school-level factors such as attendance, pupil-teacher ratio, and school infrastructure [ 9 , 26 , 27 ]. An additional list of objects that are rarely researched is spatial factors.

The current study intends to apply the existing method for assessing the geographical neighborhood factors that influence dropout incidence and to evaluate significant findings on these factors in India. We emphasize that spatial statistics can give valuable information for analyzing dropout distribution and point out its importance.

With the use of the district-level information from the UDISE+ India of 2020, a LISA cluster map is generated. Based on the thematic maps, we observed that district dropout rates were not simply random. In particular, several districts in Arunachal Pradesh, Assam, Bihar, Gujarat, Jammu & Kashmir, Jharkhand, Madhya Pradesh, Manipur, Meghalaya, Mizoram, Nagaland, Odisha, Tamil Nadu, Tripura, and Uttar Pradesh are considered to be hotspots for dropouts in the primary, upper primary and secondary levels because these clusters of neighborhoods have a low rate of promotion and a high rate of repetition.

Further investigation on hotspots reveals a correlation between the dropouts and the promotion rate and repetition rate of boys and girls. Although previous studies have linked child and family factors to a higher risk of dropout [ 28 ], the current study emphasises that districts with high repetition rates and low promotion rates may also impact dropout rates. Districts in Arunachal Pradesh, Jammu & Kashmir, Nagaland, Assam, and Bihar have stood out, particularly in these hotspots. Even though dropout rates are greater in low promotion and high repetition rate districts, this does not imply that dropout rates are also higher in these regional clusters. There are several districts where the dropout and promotion rates are inversely proportional. In addition to the few co-locational clusters, low promotion rates, and high repetition rate districts also have outliers in terms of dropout rate. So, Researchers must be cautious when making conclusions. They must also qualitatively explore why certain nearby districts have noticeably different dropout rates despite having a nearly identical promotion and repetition rates. It is feasible that the dropout rate might be less due to the standard initiatives implemented between blocks at the district level [ 29 ].

Despite being focused on Indian districts, this study’s methodology may also be employed at the block level. Understanding the spatial features and clusters of districts with greater block-level dropout rates is more required than merely identifying the districts with the highest dropout rates. In this regard, geospatial studies can assist in identifying block-level dropout hotspots to help make decisions that possess the capacity in changing district-level dropout trends.

In this study, we emphasized the importance of spatial analysis of district-level dropout rates using quartile, univariate-bivariate LISA, and spatial autoregressive models. It is possible to find the district-level variations of dropout rates using quartile maps. Clustered LISA maps displayed districts with comparable high or low dropout rates close to one another. This identification of dropout hotspots or coldspots can increase the effectiveness of contextualized interventions [ 30 ]. LISA maps highlight outliers, and finding outliers might encourage the qualitative researcher to investigate what would make a specific district differ from its neighboring districts. Additionally, spatial regressions were employed in this study to investigate the possible correlation between the dropout and promotion-repetition rates of boys and girls.

Although this study contributes some conceptual and methodological insights, it has certain limitations. Firstly, GIS-based research will always have difficulty with data availability, and this study also faced the same. Second, this study analyzed the pattern of district-level dropout rates and their influencing school-level characteristics across districts within a district-level framework. This analysis might be taken further and applied to block levels to determine the intra-district variation in student dropout rates, which could help to determine school-level factors influencing the dropout variation within districts. Third, though this study examined the regions in India with the highest dropout rates, our study only focused on spatial factors and not the causative framework. Fourth, we exclusively considered the characteristics at the school level specific to the promotion-repetition rates and excluded other factors related to child, family and community. This study is the first attempt to use spatial analysis for dropout data at the district level in India, therefore this limitation offers an opportunity for future work to expand the knowledge about how certain factors might be geographically clustered and examine regional patterns.

Despite its limitations, this study highlights the conceptual findings to advance our understanding of how dropout rates are influenced by geographical locations and methodological strategies for using GIS tools to investigate educational sector challenges. The inference that dropout rates in India are geographically grouped in specific districts in the north-eastern and central states is thematically supported by the evidence. These factors are not deterministic, although high dropout rates are correlated with low promotion rates and high repetition rates.

This study recommends and supports the use of GIS approaches that are suitable for concerns and challenges in education. Such techniques can enhance conceptual knowledge and practical implications by identifying spatially effective remedies. These conceptual and methodological contributions may motivate the researchers to explore the methodology for real-world problems including strategic planning, policy-making, and decision-making in the education sector.

Although school enrolment in India has seen a substantial increase over a few years, there is still no decrease in dropout rates. This study provides clear evidence that school dropout is a persisting issue in India affecting educational attainment progress. The results of our research indicate geospatial district-level variations in primary, upper primary, and secondary dropouts.

In addition to highlighting the geographical variations in dropout rates, this study examines the association between dropouts and promotion-repetition rates. In particular, it shows a strong negative relationship between promotion rates and school dropouts, and a positive relationship between the repetition rates and the dropouts. Based on the findings, the study recommends that India’s education policy must target the hotspot districts with high dropouts rate.

Extending the study to analyze and correlate all causal factors in hot spot districts will throw light on creating navigable pathways for comprehensive intervention strategies. In addition, the maps and tables provided in our study will provide a substantial base for policymakers to initiate conversations based on highlighted hotspots. This kind of GIS-based study can assist the first step in developing a strategy to enhance the nation’s educational infrastructure and, in turn, its economic situation.

Though the government has been consistently making efforts to improve the educational standards, the north-eastern and central districts in India need to be in a better position to provide standard education due to the high number of hotspots in this region. In the continuum to this conclusion, this study calls for rigorous preventive measures to reduce dropouts in achieving quality education. with the ultimate goal of achieving global education standards that will enable our future generations to strive and prosper more successfully on the planet.

Supporting information

S1 dataset..

https://doi.org/10.1371/journal.pone.0280034.s001

S1 Appendix.

https://doi.org/10.1371/journal.pone.0280034.s002

Acknowledgments

We thank Dr. Dhivya Karmegam, Assistant Professor, School of Public Health, SRM University, Dr. Supriya S, Research Scholar, Translational and Medicine Research, SRM University, and reviewers for comments that greatly improved the manuscript.

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The present study aims to study the risk of school dropouts in India using retrospective approach to apply Cox proportional hazard model. Using the 75th round of NSSO data, it is observed that around 74 per cent of population aged 18 years and above have dropped out from school before reaching 12th standard. The survival approach provides strong causal evidence that factors like caste division, wealth quintile, type of institution, and regional difference play a pivotal role in determining school dropouts in India. Further, no interest in education, distance from school, unable to cope up/failure in studies and financial constraint are the major reasons which elevate the risk of school dropouts. Among these reasons, no interest in education and unable to cope up/failure in studies are related to quality of education, whereas financial constraint and distance from schooling are related to poor public-school delivery in India. Among female population, marriage is an important factor of school attrition. Therefore, the study underscores the importance of better school infrastructure and quality of affordable and accessible education to improve the school enrolment for further levels of education. The study recommends implementing school-based programmes aimed at preventing early marriage among females to mitigate the risk of increased school dropout rates.

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Data availability.

The data employed in this research are open access which can be easily downloaded from NSSO website. The link is as follows: NSSO 75th: http://mospi.nic.in/unit-level-data-report-nss-75th-round-july-2017-june-2018-schedule-250social-consumption-health .

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This article is a part of PhD thesis, and I would like to thank my supervisor for his continuous support and guidance.

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Garg, M.K., Chowdhury, P. & Sheikh, I. Determinants of school dropouts in India: a study through survival analysis approach. J. Soc. Econ. Dev. 26 , 26–48 (2024). https://doi.org/10.1007/s40847-023-00249-w

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Accepted : 21 April 2023

Published : 05 June 2023

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DOI : https://doi.org/10.1007/s40847-023-00249-w

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