Sociodemographic Attributes and Dropout Intentions of Working University Students: Evidence from Estonia

Abstract

This study examines the relationship between various demographic and situational factors and working students’ decisions to change their study programmes and abandon higher education. It utilises a sample of 1902 working students derived from the Eurostudent VII survey and employs cross-tabulation and chi-square tests. The findings reveal statistically significant associations between several factors and students’ educational decisions. Males are more likely to consider abandoning higher education than females. Younger students, particularly those up to 21 years old, are more inclined to consider changing their study programmes. Financial difficulties significantly influence students’ considerations of both changing study programmes and abandoning higher education. Students in the arts, humanities, and ICT are more likely to consider abandoning their studies. Conversely, age does not significantly affect the likelihood of abandoning higher education. Parental educational attainment does not significantly influence decisions to change or abandon study programmes, whereas living situations, such as living independently and not living with parents, significantly affect changing the study programme. Qualification level affects the likelihood of changing study programmes, with bachelor’s students more likely to consider changes than masters and long-term national degree students, but it does not significantly affect the likelihood of abandoning higher education. Education–job mismatch significantly affects both changing study programmes and abandoning higher education, while the duration of working hours only influences the decision to alter study programmes. By revealing these findings, this research extends the student retention discourse as well as highlights how cultural, economic, familial, and workplace capital influence working students’ educational decisions.

1. Introduction

1.1. Background

Higher education is a critical phase in shaping individuals’ careers and personal development. In recent years, there has been an expanding concern among researchers regarding the growing number of university students in Estonia who have considered dropping out [1]. Recently, the data from Statistics Estonia [2] reveal persistent practices of students discontinuing their studies across various levels of higher education. The total number of university dropouts was 5704 in 2021 and 4522 in 2023, indicating that a noteworthy proportion of students continue to struggle with completing their education. Professional higher schools mirror this trend, with slightly different dropout figures from 1127 in 2021 to 1170 in 2023, indicating a pervasive issue across various higher education institutions.

Additionally, the data highlights gender-specific patterns in dropout numbers: males went from 2828 in 2021 to 2196 in 2023, and females went from 2876 in 2021 to 2326 in 2023. These figures show that the practice of discontinuing education exists for both genders. Notably, males are more likely to drop out of bachelor’s programmes, with numbers falling from 1432 in 2021 to 1127 in 2023, whereas females show higher dropout numbers in master’s programmes, fluctuating from 937 in 2021 to 859 in 2023. Bachelor’s programmes exhibit the highest dropout numbers, decreasing from 2772 in 2021 to 2218 in 2023. While the dropout numbers for professional and doctoral studies are lower, the persistence of these figures—1123 professional higher education dropouts in 2022 and 214 doctoral dropouts in 2023—highlights ongoing issues. For professional studies, stable dropout numbers around 540–548 for males and an increase from 521 to 556 for females indicate that even specialised, career-focused programmes are not immune to dropout challenges. The persistent dropout numbers across different levels of study, genders, and types of institutions underscore the complexity of the issue. However, the specific factors contributing to these dropouts have not been thoroughly investigated in the Estonian context, specifically for university students who concurrently juggle their education and jobs.

1.2. Relevance of the Research

Understanding the factors that influence dropout decisions can help Estonian institutions design better support systems, ultimately improving student retention and success rates. Working students face unique challenges that may affect their academic persistence, making this an important area of study. While studies have examined aspects such as financial difficulties, academic performance, and the impact of socio-economic background on student persistence [3,4,5,6], there is still a lack of clarity regarding why working university students intend to drop out.

Broad retention studies often overlook the specific difficulties that arise from juggling work and school [7]. Although working students constitute a significant portion of the university population, their specific needs and challenges are often neglected [8]. The experiences of working students vary widely, making the impact of their employment on educational outcomes complex [9,10]. Understanding sociodemographic factors is crucial, as it helps identify the specific profiles of working students who are at risk of dropping out. This understanding provides valuable insights into the retention discourse and aids in creating more customised retention interventions. While support mechanisms are available in universities, they often focus on traditional students [11,12]. Knowing the specific sociodemographic effects on dropout intentions can significantly enhance the knowledge base and include working students in broader retention strategies. Including working students in broader retention strategies requires understanding their unique context. In Estonia, research focusing specifically on the dropout intentions of working university students is particularly underexplored. The socio-economic conditions in Estonia create a unique environment for working students, yet detailed studies on their dropout intentions are scarce [1,13]. Addressing this research gap is crucial to developing targeted interventions that can support working students in Estonia more effectively. This research aims to fill this gap by providing insights into the demographic characteristics (e.g., age, gender, qualification, field of study, parental education) and situational factors (e.g., financial difficulties, living situation, working hours, education–job matching) that influence dropout intentions among working students in Estonia.

1.3. Research Question

In particular, this research intends to answer the question: What are the significant demographic and situational factors influencing working university students’ decisions to change their study programmes or abandon their higher education in Estonia?

1.4. Conceptual Clarification

The term ‘working student’ refers to individuals who combine both employment and academic study. This dual role involves managing work commitments and educational responsibilities, driven by financial needs, career goals, or personal development. However, the definition varies widely due to different interpretations of full-time and part-time work and study [7,14,15]. Working students are often considered non-traditional students, typically older than the average university student, employed, and from lower socioeconomic backgrounds [7,11]. They may also have family responsibilities and enter higher education through non-traditional routes. They are a specific group of students who often lack the cultural and social capital associated with academic success, which complicates their educational journey [16,17,18].

Studies show that working students’ experiences vary greatly due to differences in work hours, employment types, and academic disciplines [5,10,18]. Their classifications in the literature are overly simplistic and do not capture the complexities of working students’ lives [12,16]. Despite their diverse backgrounds, working students share common challenges such as balancing work and study, financial independence, family responsibilities, and a lack of cultural and social capital. For this research, working students are defined as those who combine study and employment. This straightforward definition helps keep the research objectives focused and relevant. Given the constraints of time, resources, and data availability, this definition allows for an examination of a broader group of working students. As such, using a simplified definition is practical and effective.

No matter how they are defined, the literature [16,17,18,19] has shown that working students bring a myriad of life experiences that compel them to discontinue their education. These include personal, financial, cultural, familial, and other institutional challenges. Their discontinuation often results in changing study programmes, taking breaks from studies and returning later, or abandoning higher education altogether [19]. Both the intention to change programmes and the intention to abandon study programmes completely have been considered as dropout intentions in this research. Both actions indicate a significant disruption in a student’s educational trajectory and reflect underlying challenges in maintaining their current academic path. Changing a programme often signifies a mismatch between the student’s expectations or needs and what their current programme offers. In fact, it can stem from various factors, such as dissatisfaction with the curriculum, perceived lack of relevance to career goals, or difficulties in managing workloads, in addition to sociodemographic factors. While changing a programme does not equate to leaving education entirely, it involves a significant shift that can delay progress, increase costs, and potentially lead to further disengagement if the new programme does not meet the student’s expectations either. On the other hand, the intention to abandon the study programme completely is a more definitive dropout action. It indicates a student’s decision to leave the higher education system altogether, which can be due to overwhelming personal, financial, or academic challenges. Such action has immediate and long-term consequences for the student’s career prospects and personal development. By considering both actions as dropout intentions, the research acknowledges the spectrum of detrimental practices that can disrupt a student’s educational journey. By including such a comprehensive view, this research would allow for a better understanding of the factors leading to educational discontinuity and aid in developing targeted interventions to support student retention and success.

1.5. Theoretical Framework

The theoretical foundation of this research is based on retention and dropout theories [3,20,21,22,23,24], with particular emphasis on Bourdieu’s theory of capital [25]. Pierre Bourdieu’s theory of capital provides a valuable framework for understanding the factors influencing working students’ educational decisions. Bourdieu identifies three primary forms of capital—economic, cultural, and social—that play crucial roles in shaping individuals’ educational trajectories [25]. Economic capital refers to the financial resources that students and their families possess. These resources are essential for affording tuition fees, living expenses, and other educational costs. For working students, economic capital is particularly critical, as they often juggle employment and academic responsibilities. The need to work while studying can exacerbate financial stress, making it an important factor in their decisions to change study programmes or abandon higher education altogether. Financial difficulties can lead to increased stress and dissatisfaction, prompting students to seek alternative educational paths [26,27]. Other scholars [28,29] have also highlighted the impact of economic constraints on student attrition, emphasising that financial difficulties are a major reason why students leave higher education.

Cultural capital encompasses the educational qualifications, knowledge, skills, and competencies that individuals acquire through family and educational institutions. Parents’ educational attainment is a critical component of cultural capital. Higher levels of parental education often correlate with greater academic support and higher educational aspirations for their children [30]. For working students, the ability of balancing job responsibilities with academic expectations may also affect their cultural capital. The dual burden of work and study can limit the time and energy they can devote to their academic pursuits, potentially affecting their educational outcomes [9]. As highlighted by researchers [31], cultural capital plays a significant role in academic achievement, where students from higher socio-economic backgrounds often have more access to educational resources and support.

On the other hand, social capital refers to the networks and relationships that provide individuals with support and resources [25,32]. It also includes family, friends, mentors, and institutional connections. Living situations, such as living independently or with parents, can be considered aspects of social capital in this context. For instance, working students who do not live with parents may lack immediate familial support, potentially influencing their educational decisions. Similarly, a mismatch between education and job expectations can erode students’ workplace capital, leading to dissatisfaction and the consideration of abandoning studies. Working students often rely on workplace networks and institutional support systems to manage their dual roles, which can either enhance or hinder their educational persistence, depending on the quality and extent of these networks. Researchers [33,34,35] also emphasise the importance of social capital, arguing that strong social networks can provide emotional support and practical assistance, which are crucial for student retention. Through the application of these theoretical views, this research seeks to understand the factors influencing working students’ decisions and to highlight the interplay between economic, cultural, and social dimensions in shaping educational decisions.

2. Materials and Methods

This exploratory study employs a descriptive approach to examine the socio-demographic attributes of working students in Estonia. Unlike diverse forms of research, which seek to describe or explain aspects of a phenomenon, exploratory research focuses on gaining an initial understanding and uncovering new insights [36]. While there may be existing studies on student retention and dropout rates, this research seeks to explore these issues specifically within the context of working students in Estonia, a topic that may not be well documented or thoroughly investigated. This research utilises data from the Eurostudent VII survey [37]. The Eurostudent Survey VII, conducted in Estonia from February to July 2019, provides comprehensive data on the social and economic conditions of higher education students across Europe. By using standardised questionnaires, the survey collects detailed information on students’ socio-economic backgrounds, financial situations, living conditions, study environments, and employment status. The survey received 2760 responses from Estonian university students, and out of these, 1902 were working students; this study focused on the sample of working students.

The variables used in this study (see

Table 1. Characteristics of the sample.

Variables	Frequency	Percent	Mean (Standard Deviation)
Gender:
Female	1463	76.9	1.23 (0.421)
Male	439	23.1
Age:
Up to 21 years	351	18.5	2.75 (1.130)
22 to <25 years	463	24.3
25 to <30 years	405	21.3
30 years or over	683	35.9
Parents education:			2.61 (0.606)
Low education background (ISCED 0-2)	118	6.2
Medium education level of parents (ISCED 3-4)	488	25.7
High education level of parents (ISCED 5-8)	1232	64.8
No answer	38	2.0
Don’t know	26	1.4
Qualification:
Bachelor	1098	57.7	2.54 (0.766)
Master	697	36.6
Long national degree	107	5.6
Field of study:
Education	212	11.1	4.61 (2.770)
Arts and humanities	316	16.6
Social sciences, journalism, and information	253	13.3
Business, administration, and law	367	19.3
Natural sciences, mathematics, and statistics	122	6.4
ICTs	151	7.9
Engineering, manufacturing, and construction	95	5.0
Agriculture, forestry, fisheries, and veterinary	15	0.8
Health and welfare	293	15.4
Services	75	3.9
No answer	3	0.2
Financial situation:
Students with financial difficulties	379	19.9	2.31 (0.786)
Middle category	536	28.2
Students without financial difficulties	971	51.1
No answer	16	0.8
Living situation:
Students living with parents	310	16.3	0.84 (0.369)
Students not living with parents	1592	83.7
Working hours:
1–20 h	675	35.5	1.64 (0.481)
>20 h	1181	62.1
Education–job matching:
Matched	788	41.4	1.35 (0.478)
Unmatched	429	22.6
Changing study programme:
Strongly agree	60	3.2	4.49 (0.985)
Agree	64	3.4
Neutral	129	6.8
Do not agree	276	14.5
Do not agree at all	1362	71.6
No answer	11	0.6
Completely abandoning education:
Strongly agree	42	2.2	4.62 (0.876)
Agree	53	2.8
Neutral	90	4.7
Do not agree	212	11.1
Do not agree at all	1492	78.4
No answer	13	0.7
N	1902	100

) include demographic factors such as age, gender, financial status, living situation, parental educational attainment, work status, education levels, fields of study, and education–job matching. Additionally, variables related to students’ intentions to change their study programme and abandon higher education completely were included. By incorporating these theoretically informed variables, which align with positivist epistemology [38], this research aims to provide comprehensive answers to the research question.

The analytical techniques involved cross-tabulation and nonparametric tests [39,40] to identify associations between these variables and SPSS-23 was used for the computational analysis.

Table 2. Measure of association.

Variable	Changing Study Programme	Abandoning Higher Education
Gender	Chi-square: 3.382, p = 0.496; Somers’d: −0.004, p = 0.868	Chi-square: 17.601, p = 0.001; Somers’d: −0.090, p = 0.000
Age	Chi-square: 53.179, p < 0.001; Somers’d: 0.113, p = 0.000	Chi-square: 19.715, p = 0.073; Somers’d: −0.038, p = 0.051
Parents’ educational attainment	Chi-square: 6.198, p = 0.625; Somers’d: v0.044, p = 0.038	Chi-square: 7.373, p = 0.497; Somers’d: −0.005, p = 0.832
Qualification studied	Chi-square: 28.886, p < 0.001; Somers’d: 0.079, p = 0.000	Chi-square: 13.891, p = 0.085; Somers’d: 0.007, p = 0.737
Field of study	Chi-square: 46.621, p = 0.111; Somers’d: 0.008, p = 0.648	Chi-square: 72.970, p < 0.001; Somers’d: 0.015, p = 0.387
Financial situation	Chi-square: 50.496, p < 0.001; Somers’d: 0.135, p = 0.000	Chi-square: 40.677, p < 0.001; Somers’d: 0.101, p = 0.000
Living situation	Chi-square: 17.251, p = 0.002; Somers’d: 0.051, p = 0.024	Chi-square: 0.482, p = 0.975; Somers’d: −0.009, p = 0.696
Education-job alignment	Chi-square: 62.056, p < 0.001; Somers’d: −0.201, p = 0.000	Chi-square: 16.870, p = 0.002; Somers’d: −0.085, p = 0.003
Number of hours worked	Chi-square: 12.601, p = 0.013; Somers’d: 0.046, p = 0.043	Chi-square: 5.729, p = 0.220; Somers’d: −0.030, p = 0.168

has the measure of association, while

Table 3. Cross-tabulation of the changing study programme.

		I Am Seriously Thinking about Changing My Current Main Study Programme					Total
		Strongly Agree	Agree	Neutral	Do Not Agree	Do Not Agree at All
Gender	Female	48	53	99	203	1051	1454
		3.3%	3.6%	6.8%	14.0%	72.3%	100.0%
	Male	12	11	30	73	311	437
		2.7%	2.5%	6.9%	16.7%	71.2%	100.0%
Age	Up to 21 years	24	20	23	67	213	347
		6.9%	5.8%	6.6%	19.3%	61.4%	100.0%
	22 to <25 years	15	17	35	72	322	461
		3.3%	3.7%	7.6%	15.6%	69.8%	100.0%
	25 to <30 years	10	16	31	47	301	405
		2.5%	4.0%	7.7%	11.6%	74.3%	100.0%
	30 years or over	11	11	40	90	526	678
		1.6%	1.6%	5.9%	13.3%	77.6%	100.0%
Parents’ educational attainment	Low education background (ISCED 0-2)	3	4	5	15	91	118
		2.5%	3.4%	4.2%	12.7%	77.1%	100.0%
	Medium education level of parents (ISCED 3-4)	15	16	28	61	363	483
		3.1%	3.3%	5.8%	12.6%	75.2%	100.0%
	High education level of parents (ISCED 5-8)	39	38	91	190	871	1229
		3.2%	3.1%	7.4%	15.5%	70.9%	100.0%
Qualification studied	Bachelor	47	51	73	168	753	1092
		4.3%	4.7%	6.7%	15.4%	69.0%	100.0%
	Master	11	11	46	95	530	693
		1.6%	1.6%	6.6%	13.7%	76.5%	100.0%
	Long national degree	2	2	10	13	79	106
		1.9%	1.9%	9.4%	12.3%	74.5%	100.0%
Field of study	Education	5	8	10	25	161	209
		2.4%	3.8%	4.8%	12.0%	77.0%	100.0%
	Arts and humanities	15	13	31	48	208	315
		4.8%	4.1%	9.8%	15.2%	66.0%	100.0%
	Social sciences, journalism, and information	5	8	20	39	181	253
		2.0%	3.2%	7.9%	15.4%	71.5%	100.0%
	Business, administration, and law	7	6	21	51	279	364
		1.9%	1.6%	5.8%	14.0%	76.6%	100.0%
	Natural sciences, mathematics, and statistics	8	4	7	18	84	121
		6.6%	3.3%	5.8%	14.9%	69.4%	100.0%
	ICTs	5	10	9	29	98	151
		3.3%	6.6%	6.0%	19.2%	64.9%	100.0%
	Engineering, manufacturing, and construction	4	4	9	17	61	95
		4.2%	4.2%	9.5%	17.9%	64.2%	100.0%
	Agriculture, forestry, fisheries, and veterinary	0	0	0	2	13	15
		0.0%	0.0%	0.0%	13.3%	86.7%	100.0%
	Health and welfare	7	11	18	34	222	292
		2.4%	3.8%	6.2%	11.6%	76.0%	100.0%
	Services	4	0	4	12	53	73
		5.5%	0.0%	5.5%	16.4%	72.6%	100.0%
Financial situation	Students with financial difficulties	20	15	42	61	238	376
		5.3%	4.0%	11.2%	16.2%	63.3%	100.0%
	Middle category	15	21	43	97	357	533
		2.8%	3.9%	8.1%	18.2%	67.0%	100.0%
	Students without financial difficulties	24	25	43	117	759	968
		2.5%	2.6%	4.4%	12.1%	78.4%	100.0%
Living situation	Students living with parents	9	22	23	48	206	308
		2.9%	7.1%	7.5%	15.6%	66.9%	100.0%
	Students not living with parents	51	42	106	228	1156	1583
		3.2%	2.7%	6.7%	14.4%	73.0%	100.0%
Education–job alignment	Matched	11	24	25	90	631	781
		1.4%	3.1%	3.2%	11.5%	80.8%	100.0%
	Unmatched	29	20	38	73	267	427
		6.8%	4.7%	8.9%	17.1%	62.5%	100.0%
Number of hours worked	1–20 h	31	23	39	112	467	672
		4.6%	3.4%	5.8%	16.7%	69.5%	100.0%
	>20 h	27	37	84	159	869	1176
		2.3%	3.1%	7.1%	13.5%	73.9%	100.0%

and

Table 4. Cross-tabulation of the abandonment of study programme.

		I Am Seriously Thinking of Completely Abandoning My Higher Education Studies					Total
		Strongly Agree	Agree	Neutral	Do Not Agree	Do Not Agree at All
Gender	Female	27	39	61	148	1177	1452
		1.9%	2.7%	4.2%	10.2%	81.1%	100.0%
	Male	15	14	29	64	315	437
		3.4%	3.2%	6.6%	14.6%	72.1%	100.0%
Age	up to 21 years	13	10	14	32	278	347
		3.7%	2.9%	4.0%	9.2%	80.1%	100.0%
	22 to <25 years	8	9	16	41	386	460
		1.7%	2.0%	3.5%	8.9%	83.9%	100.0%
	25 to <30 years	8	15	20	56	304	403
		2.0%	3.7%	5.0%	13.9%	75.4%	100.0%
	30 years or over	13	19	40	83	524	679
		1.9%	2.8%	5.9%	12.2%	77.2%	100.0%
Parents’ educational attainment	Low education background (ISCED 0-2)	3	3	6	12	94	118
		2.5%	2.5%	5.1%	10.2%	79.7%	100.0%
	Medium education level of parents (ISCED 3-4)	14	16	17	51	385	483
		2.9%	3.3%	3.5%	10.6%	79.7%	100.0%
	High education level of parents (ISCED 5-8)	19	30	65	145	968	1227
		1.5%	2.4%	5.3%	11.8%	78.9%	100.0%
Qualification studied	Bachelor	31	31	45	120	863	1090
		2.8%	2.8%	4.1%	11.0%	79.2%	100.0%
	Master	10	19	42	86	536	693
		1.4%	2.7%	6.1%	12.4%	77.3%	100.0%
	Long national degree	1	3	3	6	93	106
		0.9%	2.8%	2.8%	5.7%	87.7%	100.0%
Field of study	Education	5	5	10	17	173	210
		2.4%	2.4%	4.8%	8.1%	82.4%	100.0%
	Arts and humanities	11	9	17	41	237	315
		3.5%	2.9%	5.4%	13.0%	75.2%	100.0%
	Social sciences, journalism, and information	4	10	6	38	194	252
		1.6%	4.0%	2.4%	15.1%	77.0%	100.0%
	Business, administration, and law	6	6	13	36	302	363
		1.7%	1.7%	3.6%	9.9%	83.2%	100.0%
	Natural sciences, mathematics, and statistics	4	2	7	9	99	121
		3.3%	1.7%	5.8%	7.4%	81.8%	100.0%
	ICTs	7	10	12	27	94	150
		4.7%	6.7%	8.0%	18.0%	62.7%	100.0%
	Engineering, manufacturing, and construction	0	4	9	16	66	95
		0.0%	4.2%	9.5%	16.8%	69.5%	100.0%
	Agriculture, forestry, fisheries and veterinary	0	0	0	2	13	15
		0.0%	0.0%	0.0%	13.3%	86.7%	100.0%
	Health and welfare	3	5	14	21	249	292
		1.0%	1.7%	4.8%	7.2%	85.3%	100.0%
	Services	1	2	2	5	63	73
		1.4%	2.7%	2.7%	6.8%	86.3%	100.0%
Financial situation	Students with financial difficulties	18	18	24	50	264	374
		4.8%	4.8%	6.4%	13.4%	70.6%	100.0%
	Middle category	12	14	33	53	422	534
		2.2%	2.6%	6.2%	9.9%	79.0%	100.0%
	Students without financial difficulties	11	19	32	107	798	967
		1.1%	2.0%	3.3%	11.1%	82.5%	100.0%
Living situation	Students living with parents	7	7	15	33	245	307
		2.3%	2.3%	4.9%	10.7%	79.8%	100.0%
	Students not living with parents	35	46	75	179	1247	1582
		2.2%	2.9%	4.7%	11.3%	78.8%	100.0%
Education-job alignment	Matched	9	19	34	70	648	780
		1.2%	2.4%	4.4%	9.0%	83.1%	100.0%
	Unmatched	17	8	26	52	324	427
		4.0%	1.9%	6.1%	12.2%	75.9%	100.0%
Number of hours worked	1–20 h	18	13	30	69	542	672
		2.7%	1.9%	4.5%	10.3%	80.7%	100.0%
	>20 h	22	38	60	140	914	1174
		1.9%	3.2%	5.1%	11.9%	77.9%	100.0%

contain cross-tabulations.

3. Results

3.1. Demographic Profile of Working Students

The age range (See Table 1) of the pupils spans a wide spectrum, encompassing both young adults (mean age category: 22 to <25 years) and individuals over the age of 30. In particular, 35.9% of the students fall into the age category of 30 years or older. The age group of individuals between 22 and under 25 years accounts for 24.3%, and students aged 25 to under 30 years make up 21.3%. Students aged 21 and under make up 18.5% of the total. The age distribution indicates that the working student population include not just young university students but also a substantial portion of mature adults who may be pursuing higher education at a later stage in life or undertaking further study. Regarding gender distribution, males account for 23.1% of the student population, while females represent 76.9%. The tendency towards female students could point to a greater female student population generally or reflect more general patterns in higher education enrolment by gender in Estonia.

Additionally, a significant majority of the students, 57.7%, are enrolled in bachelor’s degree programmes (ISCED 6), indicating a strong focus on undergraduate education. Meanwhile, 36.6% are pursuing master’s degree programmes (ISCED 7), and 5.6% are in long national degree programmes exceeding three years. Regarding fields of study, the most common areas include business, administration, and law (19.3%); followed by arts and humanities (16.6%); health and welfare (15.4%); and social sciences, journalism, and information (13.3%). Less represented fields include agriculture, forestry, fisheries and veterinary science and engineering, manufacturing, and construction, indicating a trend towards business, arts, and health-related studies.

The educational attainment of the students’ parents tends to be higher, with 67% having parents with a high education level (ISCED 5-8). Students with parents who have a medium education level (ISCED 3-4) account for 26.6%, while only 6.4% have parents with a low education background (ISCED 0-2). This means that, despite some students coming from lower economic backgrounds, the majority hail from families with higher educational attainment.

The financial situation of working students varies widely. According to the data (Table 1), 379 students (19.9%) face financial difficulties, with a mean score of 2.31, indicating moderate financial strain. Meanwhile, 971 students (51.1%) do not experience financial difficulties and have sufficient financial support. These figures highlight the diverse economic backgrounds of working students, as well as the significant issue of financial difficulties for nearly a fifth of the sample.

The living situation of working students shows a clear distinction between those living with parents and those living independently. According to the data, 310 students (16.3%) live with their parents, while 1592 students (83.7%) do not. The mean score is 0.84 with a standard deviation of 0.369, indicating that the majority of students live independently, reflecting a higher level of financial responsibility and autonomy. This financial burden may lead them to alter their course of study or perhaps drop out of university entirely.

The working status of students reveals differences in the number of hours worked. According to the data, 675 students (35.5%) work between 1 and 20 h per week, while 1181 students (62.1%) work more than 20 h per week. The mean score is 1.64 with a standard deviation of 0.481, indicating a considerable portion of students are working substantial hours alongside their studies. Table 1 also indicates that 41.4% of these students have employment that corresponds to their field of study, while 22.6% have jobs that do not.

3.2. Reasons for Working

The result (see Figure 1) reveals various reasons why students choose to work while studying, reflecting their diverse motivations and needs. A significant majority of students work to cover their living costs, with 65.3% indicating that this applies totally to their situation. Additionally, 13.6% somewhat agree, while 8.7% are neutral. Only 12.4% of students somewhat or totally disagree with this statement. It underscores the financial pressures many students face, compelling them to work to sustain their basic living expenses. Furthermore, nearly half of the students, 48.9%, work to gain experience in the labour market. It is complemented by 18.3% who somewhat agree, and 13.2% who are neutral. A smaller portion, 19.5%, somewhat or totally disagree. In terms of financial necessity, 36.8% of students totally agree that without their paid job, they could not afford to be students. An additional 9.7% somewhat agree, while 12.5% are neutral. However, 41.0% of students disagree to varying extents. It indicates that for many students, employment is crucial for continuing their education, although a notable portion can manage without it.

Some students work to support others financially, with 22.5% totally agreeing and another 8.7% somewhat agreeing. Meanwhile, 9.5% are neutral, and a substantial 47.6% do not agree at all. It suggests that while a significant number of students have financial dependents, the majority do not face this additional responsibility. Similarly, 48.6% of students work to afford things they otherwise could not buy, with 22.4% somewhat agreeing and 14.4% being neutral. Only 14.7% somewhat or totally disagree. These insights emphasise the significant role that employment plays in the lives of students and the diverse motivations behind their decision to work.

3.3. Association between Socio-Demographic Factors and Dropout Intentions

Table 2 highlights the relationship between various demographic and situational factors of students and their consideration of changing their study programme or abandoning higher education completely. In the analysis, the values of Somers’d and chi-square tests reveal several important relationships. Table 3 and Table 4 also provide relevant results.

Regarding gender, female students are less likely to consider changing their study programme (72.3% do not agree at all) compared to male students (71.2% do not agree at all). Similarly, male students are more likely to consider abandoning their studies (3.4% strongly agree) compared to female students (1.9% strongly agree). Gender shows a statistically significant association with abandoning higher education (chi-square: 17.601, p = 0.001; Somers’d: −0.090, p = 0.000) but not with changing the study programme (chi-square: 3.382, p = 0.496; Somers’d: −0.004, p = 0.868).

In contrast, younger students (up to 21 years) are more likely to think about changing their programme (6.9% strongly agree) compared to older students (30 years or over, 1.6% strongly agree). They are also more inclined to consider abandoning their studies (3.7% strongly agree) compared to older students (1.9% strongly agree). Age significantly influences changing the study programme (chi-square: 53.179, p < 0.001; Somers’d: 0.113, p = 0.000) but not abandoning higher education (chi-square: 19.715, p = 0.073; Somers’d: −0.038, p = 0.051). It suggests that younger students may be more uncertain or dissatisfied with their initial academic choices.

The educational attainment of parents did not show a significant effect on students’ thoughts about changing their study programme. This implies that students’ considerations of changing their programme are relatively independent of their parents’ educational backgrounds. Similarly, there is no significant relationship between the educational attainment of parents and students’ thoughts about abandoning their studies. The Pearson chi-square value is 7.373 with a p-value of 0.497, indicating that this factor does not significantly influence students’ considerations of abandoning their studies.

The findings show that bachelor’s students are more likely to contemplate changing their study programme than master’s and long national degree students, highlighting potential dissatisfaction or a higher level of indecision among undergraduate students. Additionally, the qualification studied significantly impacts changing the study programme (chi-square: 28.886, p < 0.001; Somers’d: 0.079, p = 0.000) but not abandoning higher education (chi-square: 13.891, p = 0.085; Somers’d: 0.007, p = 0.737).

Additionally, the findings show that students in arts and humanities (4.8% strongly agree) and ICTs (3.3% strongly agree) are more likely to consider changing their programme than those in education (2.4% strongly agree) or business, administration, and law (1.9% strongly agree). Similarly, students in ICTs (4.7% strongly agree) and arts and humanities (3.5% strongly agree) are more likely to consider abandoning their studies than those in education (2.4% strongly agree) or business, administration, and law (1.7% strongly agree). The field of study significantly affects abandoning higher education (chi-square: 72.970, p < 0.001) but not changing the study programme (chi-square: 46.621, p = 0.111).

The findings show that students with financial difficulties are more likely to consider changing their programme (5.3% strongly agree) compared to those without financial difficulties (2.5% strongly agree). They are also more likely to consider abandoning their studies (4.8% strongly agree) compared to those without financial difficulties (1.1% strongly agree). Financial situation significantly influences both changing the study programme (chi-square: 50.496, p < 0.001; Somers’d: 0.135, p = 0.000) and abandoning higher education (chi-square: 40.677, p < 0.001; Somers’d: 0.101, p = 0.000).

Furthermore, students not living with parents are more inclined to consider changing their programme (3.2% strongly agree) than those living with parents (2.9% strongly agree). However, living situation has a smaller effect on the intention to abandon studies, with students living with parents (2.3% strongly agree) being slightly more inclined compared to those not living with parents (2.2% strongly agree). Living situation significantly affects changing the study programme (chi-square: 17.251, p = 0.002; Somers’d: 0.051, p = 0.024) but not abandoning higher education (chi-square: 0.482, p = 0.975; Somers’d: −0.009, p = 0.696).

Regarding education job alignment, the findings show that students with unmatched jobs are more likely to consider changing their programme (6.8% strongly agree) compared to those with matched jobs (1.4% strongly agree). They are also more inclined to abandon their studies (4.0% strongly agree) compared to those with matched jobs (1.2% strongly agree). Education–job alignment significantly influences both changing the study programme (chi-square: 62.056, p < 0.001; Somers’d: −0.201, p = 0.000) and abandoning higher education (chi-square: 16.870, p = 0.002; Somers’d: −0.085, p = 0.003).

The number of hours students work per week significantly affects their likelihood of considering a change in their study programme. Students working 1–20 h per week show a higher tendency to change their programme (4.6% strongly agree) compared to those working more than 20 h per week (2.3% strongly agree). Similarly, students working 1–20 h per week are more likely to consider abandoning their studies (2.7% strongly agree) compared to those working more than 20 h per week (1.9% strongly agree). The number of hours worked significantly affects changing the study programme (chi-square: 12.601, p = 0.013; Somers’d: 0.046, p = 0.043) but not abandoning higher education (chi-square: 5.729, p = 0.220; Somers’d: −0.030, p = 0.168)

4. Discussion

The goal of this study was to answer the question: What are the significant demographic and situational factors influencing working university students’ decisions to change their study programmes or abandon their higher education in Estonia? To achieve this, the study employed quantitative techniques to analyse the data and generate the findings. In particular, the study identified the association between changing study programme and abandoning higher education completely with demographic characteristics (e.g., age, gender, qualification, field of study, parental education) and situational factors (e.g., financial difficulties, living situation, working hour, education-job alignment).

The findings provide important insights into the factors influencing educational decisions among working university students in Estonia, aligning with a broader discussion while highlighting specific contextual settings. The study reveals a gender disparity in the likelihood of abandoning higher education, with males being more likely to drop out than females. It aligns with global trends where male students often show higher dropout rates, possibly due to societal expectations and pressures to join the workforce early, as noted by researchers [41]. In the Estonian context, it might reflect cultural attitudes towards gender roles and education, emphasising the need for targeted interventions to support specific students. Younger students, particularly those up to 21 years old, are more inclined to consider changing their study programmes. The finding suggests a phase of exploration and uncertainty common among younger students who are still developing their academic and career identities, contradictory with researchers’ [42] findings on student retention. However, age does not significantly affect the likelihood of abandoning higher education, indicating that the decision to drop out may be influenced more by situational factors than by age alone.

Financial difficulties are a critical factor influencing both the consideration of changing study programmes and abandoning higher education. This finding supports Bourdieu’s theory of economic capital, which posits that financial resources are crucial to educational persistence [25]. In Estonia, where the cost of living and tuition can be burdensome, financial support mechanisms are crucial for reducing dropout rates. Addressing this issue requires a comprehensive evaluation of existing financial aid programmes. The current financial aid options, such as need-based aid, may be insufficient and not always accessible to the working students who need them most. Similarly, while student loans, scholarships, and grants are beneficial, they might not be adequately effective for working university students. These financial aid measures often focus broadly on traditional students, potentially overlooking the specific realities and challenges faced by those who juggle work and study. As a result, many working students continue to struggle under the weight of financial burdens, making it difficult for them to sustain their educational pursuits. This oversight can contribute to higher dropout rates and hinder students’ ability to achieve their academic and professional goals. Nonetheless, exploring how universities and the government can enhance their support for working students could involve investigating best practices from other countries or institutions. For instance, some universities offer tailored financial literacy programmes to help students manage their finances better or emergency funds for students facing unexpected financial crises. Additionally, government policies that provide tax benefits or subsidies for working students could be considered to ease their financial burdens.

Moreover, the findings highlight the necessity of providing tailored support for specific fields of study. Students in certain fields, such as the arts, humanities, and ICTs, are more likely to consider changing their programmes or abandoning their studies. This could be due to perceived or real challenges in these fields, such as job market uncertainties, the demanding nature of these fields, and the potential for lucrative employment opportunities even without a completed degree. Interestingly, it raises an important point of discussion: whether there are sufficient opportunities to combine study and work in these fields, to what extent students are taking advantage of these opportunities, and whether these opportunities effectively meet the diverse needs of working students.

Furthermore, the study finds that parental educational attainment and living situation do not significantly influence decisions to abandon study programmes. It contrasts with literature [30] suggesting that parental education often correlates with student success. In Estonia, this may suggest a higher education system in which students’ decisions are more influenced by their immediate financial and academic experiences than by their familial background. However, the cultural capital provided by a parent’s higher educational background does not appear to significantly influence students’ decisions in this context, suggesting that other forms of support may be compensating. Bachelor’s students are more likely to consider changing their study programmes than master’s and long-term national degree students, suggesting higher levels of uncertainty or dissatisfaction among undergraduates. It also fits the notion of cultural capital, whereby undergraduate students might still be developing the required skills and knowledge to make confident academic and professional decisions.

Additionally, an education–job mismatch significantly affects both changing study programmes and abandoning higher education, which emphasises the need to match educational programmes with labour market demands, since misalignment may lead to frustration as stated by researchers [43]. The duration of working hours only influences the decision to alter study programmes, not to discontinue higher education. Students working fewer hours are more likely to contemplate changing their study programmes, possibly because they have more time to reassess their academic choices or to reflect on their academic dissatisfaction. In contrast, students working more hours might feel more entrenched in their current situation due to financial necessities. Those with heavier work commitments do not have the luxury to consider changes that might benefit their education in the long run. It, indeed, highlights the complexity of balancing work and study and suggests that institutional roles are crucial for student retention, consistent with the assertions of other researchers [34].

5. Conclusions

This study examines the relationship between various demographic and situational factors and working students’ decisions to change their study programmes or abandon higher education, utilising data from the Eurostudent VII survey. It contributes to the discourse on student retention and capital theories by providing fresh insights from the Estonian context. By analysing a range of factors, including age, gender, financial difficulties, and educational background, this research highlights how cultural, economic, familial, and workplace capital influence students’ educational trajectories. For instance, the finding that financial difficulties significantly influence students’ decisions aligns with Bourdieu’s theory of economic capital, underscoring the importance of financial resources in educational persistence. Similarly, the lack of significant influence from parental education suggests a more complex interplay of factors than previously understood, indicating that in Estonia, immediate financial and academic experiences may outweigh inherited cultural capital. These empirical insights enhance the understanding of the specific challenges faced by working students in Estonia and provide a basis for more targeted policy interventions and support mechanisms.

However, this research also has some limitations. Firstly, the data used in this study is cross-sectional, which means it captures a single point in time and cannot establish causality. Longitudinal data would be needed to track changes and trends over time to better understand the dynamics of students’ decisions. Secondly, the study focuses on working students in Estonia, which may limit the generalizability of the findings to other contexts or countries with different educational systems and socio-economic conditions. Third, this research has used dropout intentions, not actual dropout rates. Retention, attrition, persistence, dropout intentions, and dropout rate are distinct yet interconnected terms used to measure continuity in educational and organisational contexts. Retention refers to the institution’s ability to keep its students or employees over time, indicating overall stability. Attrition, on the other hand, measures the reduction in numbers caused by individuals leaving, indicating institutional turnover. Persistence focuses on individual commitment, highlighting a person’s continued effort to remain in a programme or job despite challenges. Dropout intentions indicate an individual’s likelihood or plans to leave, providing insight into potential future attrition. Although these differences exist, this research uses dropout intentions with a focus on working students’ perceptions. Fourth, it is important to note that the study is correlational, not causative. Finally, while the study incorporates various demographic and situational factors, there may be other relevant variables not included in the analysis, such as mental health or personal well-being, which could also significantly affect students’ decisions.