Salary Returns of Higher Education and Determinants of University Dropout among Beneficiaries of a Peruvian State Scholarship Programme

1. Introduction

The Peruvian economy has experienced notable growth over the last decade; however, the COVID-19 pandemic triggered a significant contraction in 2020. The World Bank [1] reported a rebound in 2021, with the economy growing by 13.3%, yet challenges such as inflation have negatively impacted families’ purchasing power. In 2022, the National Institute of Statistics and Informatics (INEI) [2] indicated that multidimensional poverty affects 25.9% of the population, underscoring the persistent inequality in the country.

Higher education is crucial for Peru’s socioeconomic development, enhancing job opportunities and stimulating economic growth. A study by the Ministry of Education of Peru [3] found that university graduates earn, on average, 150% more than individuals with only secondary education. Additionally, initiatives like Beca 18 from PRONABEC (National Scholarship and Educational Loan Programme) have been instrumental in expanding access to higher education, enabling young people from diverse socioeconomic backgrounds to pursue their studies. Investment in higher education also fosters innovation and competitiveness, which are vital for the country’s advancement.

Despite these advantages, higher education in Peru faces significant challenges that necessitate policies aimed at promoting equal opportunities and improving educational quality. While the massification of higher education has increased access to university education, deep-rooted inequalities remain. High-quality education often comes with a steep price, making it less accessible, whereas lower-quality education, though more affordable, perpetuates social inequity [4, 5, 6, 7].

Completing university studies has a notable impact on individuals’ income. An additional year of education can increase income by an average of 18.6%, while finishing a university degree can boost income by 29.6% or more [8, 9, 10]. In the Peruvian context, an extra year of higher education results in a 15.9% increase in income for those who complete their studies, compared to 3.9% for those who do not [11, 12].

However, university dropout remains a persistent problem in the country, influenced by both socioeconomic and academic factors. Although scholarships and financial support have proven effective in mitigating dropout, the issue persists [13, 14, 15]. Educational policies, therefore, should focus not only on improving the quality of higher education but also on reducing dropout rates to maximise the benefits associated with completing university studies [16, 17].

Since 2012, the National Scholarship and Educational Credit Programme, known as Beca 18, has played a crucial role in improving access to higher education for low-income youth in Peru. This programme has significantly increased the likelihood that these young people enrol in high-quality universities and has strengthened their educational trajectory [18, 19]. However, research on the impact of Beca 18 is still limited and often based on small samples, highlighting the need for broader and more comprehensive studies [20, 21, 22].

Studies on dropout within the framework of Beca 18 have identified significant economic barriers and adaptation challenges as key factors affecting beneficiaries [23, 24]. In response, efforts have been made to decentralise the programme, resulting in 71% of scholarship recipients in 2021 coming from regions outside the capital [25].

In this context, this research aims to evaluate the impact of completing university higher education on salary returns in Peru, as well as to identify the factors that increase the likelihood of losing a scholarship before graduating among young beneficiaries of the Peruvian state programme Beca 18.

2. Completion of higher university education and salary returns

This section focuses on estimating the association between the completion of higher university education and salary returns. It allows for a comparison of the Peruvian context with extensive evidence from various countries regarding the impact of higher education—especially its completion—on salaries.

To estimate the salary returns of higher education in Peru, we utilised the National Household Survey (ENAHO) data from 2014 to 2021. This dataset includes a sample of 34,964 observations, comprising individuals with completed higher education (21,558) and those without it (13,406). The sample is probabilistic, stratified, and multi-stage, ensuring robust representation.

The analysis employed the Mincer model [26], with adjustments based on Heckman’s methodology [27], to address selection bias. This approach connects income to years of education and work experience. To correct for selection bias, a logit model was used to estimate the probability of labour force participation, followed by the application of the inverse Mills ratio.

The final equation used to estimate salary returns is:

where log_salin_real is the deflated monthly income, a_edu is the years of education, experiencia and experiencia2 represent work experience and its square, log_H1 is the logarithm of the hours worked, mujer indicates female gender, LM represents Metropolitan Lima, ECO is the economic sector, urbano indicates the urban area, anio is the year of the survey, tipo_ocupacion describes the type of occupation, carrera is the professional category, privada indicates if the university is private, log_imr1 is the inverse of the Mills ratio, and cal_scimago represents the ranking of the university according to SCImago Institutions Rankings (SCIMAGO).

The model revealed statistically significant regression coefficients. We conducted tests for multicollinearity, heteroscedasticity, and error normality, all of which confirmed the model’s validity. To correct for self-selection bias, we used the inverse Mills ratio. Additionally, due to the large sample size, we assumed normality in the residuals.

Table 1 shows that salary returns for university education in Peru increased from 11.6 to 16.7% between 2014 and 2021 for those who completed their studies. In contrast, for those who did not complete higher education, the return increased from 2.5 to 2.8%. This pattern of higher returns associated with completing higher education has also been documented in other countries, including Colombia [10, 28], the Dominican Republic [9], and Argentina [8].

The results from the 2014 to 2021 pooled data (Table 2) show that, ceteris paribus, the rate of return to university education is 12.2%. This means that each additional year of university education corresponds to a 12.2% increase in the monthly income of individuals who attain this level of education. However, among those who completed higher education, an additional year of education results in a 15.9% increase in their monthly income, while for those who did not complete their higher education, the increase is only 3.8%.

These findings align with those reported by Adrogué [8] in Argentina, who noted higher returns for individuals with completed higher education compared to those without. Similarly, Parodi et al. [9] found in the Dominican Republic that obtaining a university degree led to a 29.6% increase in returns, suggesting a “diploma effect”, where university credentials are perceived as a strong indicator of productivity. Additionally, Sánchez et al. [10] discovered in Colombia that individuals with completed university studies could earn up to 122% more in labour income compared to those without higher education.

The results also reveal that an additional year of work experience yields a salary return of approximately 3.2% for individuals with a complete university education and 3.4% for those with incomplete higher education. Holding other factors constant, the parameter for experience (β2) is positive, while the parameter for the square of experience (β3) is negative, as anticipated by Mincer [26]. This indicates that, although income increases with experience (β2), the rate of increase diminishes due to β3, reflecting the concave relationship between experience and income. Other studies have reported slightly lower returns. For instance, Fuentes Pincheira and Herrera Cofré [29] found in Chile that each additional year of work experience results in a 2.8% salary increase, while Tarazona Quintero and Remolina Amórtegui [30] observed a 2.2% increase in Colombia.

Table 2 also reveals that, in terms of elasticities, a percentage increase in hours worked generates a positive variation in income. Ceteris paribus, a 10% increase in hours worked contributes to a 4.8% increase in income for those who completed higher university education, while for those with incomplete higher education, the return is 6.2%. It is likely that, for those with incomplete university education, overtime acts as compensation for salary differences compared to those who completed university (16.7 vs. 2.8% in returns). Barragán Codina et al. [31] found in Mexico that those who completed higher education earn a salary 86% higher per hour than those who did not complete this level.

Moreover, it is observed that being a woman reduces income compared to men: by 4.5% among those who completed their university studies and by 12.1% among those who did not complete them. These results are consistent with the trend of higher returns for men compared to women, as reported by Fuentes Pincheira and Herrera Cofré [29] for Chile, who found that a woman’s future income would decrease by 46.9%. In Colombia, Tarazona and Remolina [30] found a 23.8% decrease in women’s salaries compared to those of men, suggesting that this difference should be explained within a broad multidisciplinary framework, including sociological aspects.

These results contrast with those reported by Parodi et al. [9], who found higher return rates for women in the Dominican Republic. Additionally, this study reveals that residing in Metropolitan Lima results in a 27.2 and 27.6% higher return in income for individuals with complete and incomplete higher education, respectively, compared to those living in other regions of the country. It also shows that residents in urban areas experience returns of 12.8 and 19.8% higher than those in rural areas for complete and incomplete university studies, respectively. These findings are consistent with those of Parodi et al. [9] and Vargas Urrutia [32], who observed higher returns in urban areas in the Dominican Republic and Colombia, respectively. In contrast, Ordaz Díaz [33] found in Mexico that education was more profitable in rural areas than in urban ones.

Regarding occupation type, three categories are presented: productive unit (own business), dependent, and independent. Being dependent or independent reduces income returns compared to productive units, both for complete and incomplete university education. This variable is associated with the formality and informality of the activity performed, as well as with deductions for tax rates and other contributions. Table 2 shows that, ceteris paribus, independent workers experience a 91.4 and 89.5% loss in income (for complete and incomplete university education, respectively) compared to those with their own business. Similarly, dependent workers show a loss of 24.8 and 45.4% in income for complete and incomplete university education, compared to those with their own business.

In relation to economic activity, for those who completed university education, working in mining represents 107.7% more income than in agriculture (taking agriculture as the base comparison in the dummy variable analysis to avoid perfect multicollinearity). Working in state entities, construction, and other services represents 82.5, 79.7, and 76.0% more income than in agriculture, respectively. Smaller impacts are observed in transport and communications (61.4%), manufacturing (53.8%), commerce (42.6%), and finally, hotels and restaurants (31.7%). For those with incomplete higher education, working in mining represents 94.9% more income than in agriculture. Working in the state entities provides 71.5% more income than in agriculture, while other services, construction, and transport and communications offer increases of 54.1, 52.8, and 51.2%, respectively. Manufacturing, commerce, and hotels and restaurants offer increases of 38.4, 36.8, and 21.7% above agriculture.

In the review of salary returns by type of career, for complete university education, engineering, industry, and construction careers stand out (45.6%), followed by agricultural and veterinary careers (34.8%), compared to education careers. To a lesser extent, health sciences, natural, exact, and computing sciences, and social sciences, commerce, and law careers are found, with returns of 30.0, 29.2, and 28.7%, respectively. For incomplete university education, social sciences, commerce, and law stand out (22.2%), followed by engineering, industry, and construction (19.9%). To a lesser extent, agricultural and veterinary careers (19.5%), humanities and arts (16.6%), and health sciences (17.2%) are observed. These results reflect a particular issue with the low returns of education careers compared to other university careers.

These findings are consistent with those of Yamada and Castro [34], who observed the highest returns in medicine and engineering careers (17.7 and 16.4%, respectively), while the lowest returns were found in pedagogy and social sciences (11.2 and 12.3%, respectively). Yamada [35] further identified that the highest-earning professionals were civil engineers, economists, business administrators, and IT professionals, whereas primary education teachers received the lowest remunerations.

Studying at a private university yields higher returns compared to studying at public universities. The results indicate that among those who completed their university education, returns are 4.0% higher for private university graduates. Among those who did not complete university education, returns are 17.5% higher for those who attended private universities, suggesting that even those who did not finish their studies at a private university earn more than their counterparts from public institutions. These findings align with those of Yamada and Castro [34], who reported higher returns for private university education compared to public university education (17.9 vs. 15.2%).

Finally, considering the SCIMAGO quality ranking and the top 13 universities in Peru in 2021, it was found that individuals who completed or partially completed their university education at these institutions achieved returns of 10.6 and 8.91% in income, respectively. Yamada et al. [36], who measured the quality of educational institutions based on flexibility in access requirements, observed that attending a high-quality institution has a positive effect of over 17% on salary. This effect accounts for 40% of the salary gap between those who attended a high-quality university versus a lower-quality one, with the remaining 60% explained by pre-access factors, such as socioeconomic status, parents’ education level, and gender, among others.

3. Determinants of university dropout due to loss of state scholarships: An analysis of the Beca 18 programme

The study population comprises beneficiaries of Peru’s National Scholarship and Educational Loan Programme (PRONABEC), specifically those within the “Beca 18” scheme. Established in 2012, Beca 18 has been instrumental in funding higher education for academically outstanding and economically disadvantaged youth. The scholarship extends its support to various vulnerable groups, including orphans, individuals from communities affected by violence and drug trafficking, indigenous populations, military service veterans, and students in Intercultural Bilingual Education programmes.

Beca 18 represents a significant advancement in higher education funding in Peru. Unlike its predecessor, which primarily provided financial aid on a more limited basis, Beca 18 offers comprehensive support. This includes coverage for tuition, accommodation, and other essential living expenses, making higher education more accessible for students from vulnerable backgrounds [25, 37]. The programme has also evolved to include targeted scholarships for specific communities, such as those from Amazonian and Afro-Peruvian backgrounds, reflecting a commitment to addressing the educational needs of diverse groups [25].

A notable development in the Beca 18 programme is the introduction of a standardised entrance exam in 2016. This examination was designed to uniformly assess applicants, ensuring a fair and consistent evaluation of their academic potential and socioeconomic needs [38]. Over time, the criteria for awarding scholarships have been refined to guarantee that participating institutions meet higher standards of quality, thus enhancing the overall educational experience and outcomes for students.

The analysis focuses on beneficiaries who received the Beca 18 scholarship between 2012 and July 2019, encompassing a total of 22,150 cases. After excluding 225 cases due to reasons, such as death or document falsification, the final study population consists of 21,925 beneficiaries. This group is characterised by a common background of poverty and high academic achievement, making them ideal subjects for studying the impact of the scholarship programme. The analysis considers a range of sociodemographic, academic, and institutional variables, aiming to provide a comprehensive understanding of the programme’s effects and the factors influencing scholarship retention and success.

Data analysis was performed in multiple stages. Initially, a univariate analysis was conducted to characterise the population and identify key variables associated with scholarship loss. Subsequently, a bivariate analysis was carried out using contingency tables and Pearson’s chi-square test to evaluate the independence between variables. The null hypothesis (H0) stated that the observed frequencies would equal the expected frequencies (Φij = Φi. . Φ.j), while the alternative hypothesis (H1) proposed a difference between observed and expected frequencies (Φij ≠ Φi ..Φ.j). Expected frequencies were calculated using the formula feij = (fi.·f.j)/f, and the chi-square value was determined with the equation χ^2 = Σi = 1mΣj = 1n (fij − feij)^2/feij. The null hypothesis was rejected if the chi-square value exceeded the critical value χ²(α,(m-1)(n-1)) or if the p-value was less than 0.05.

Finally, a Probit model was employed to estimate the probability of scholarship loss, represented as P(y = 1/x) = G (β0 + β1x1 + … + βkxk) = G (β0 + xβ), where G denotes the cumulative distribution function of the standard normal distribution. The marginal effect of each variable on the probability was calculated using the formula ∂p(x)/∂xj = g (β0 + xβ) βj, where g(z) ≡ dG/dz(z). The model’s goodness-of-fit was assessed using a confusion matrix and the receiver operating characteristic (ROC) curve, with an area under the curve (AUC) of 68.85%, indicating a satisfactory level of data classification.

4. Discussion

This study critically examines higher education financing policies in Latin America, focusing on Peru, and highlights their implications for equal opportunities. The findings confirm that individuals who complete higher education earn significantly more than those who do not, emphasising the necessity of not only obtaining a degree but also completing it. This aligns with research in other Latin American countries, such as Brazil and Colombia, where higher education completion is associated with improved economic outcomes [8, 9, 10, 28]. However, university dropout persists, particularly due to economic reasons, highlighting the need to enhance support programmes to reduce dropout rates and maximise the social return on educational investment.

In Peru, the analysis reveals that wage returns vary by sector, with the mining industry benefiting from a favourable international price cycle. However, the impact in rural areas is limited due to reliance on urban supply chains, a pattern also observed in countries like Chile, where mining benefits urban economies more than rural communities [39, 40]. Gender disparities in earnings are notable, with men earning more, particularly among those with incomplete degrees. This aligns with findings from Colombia and Chile, where similar gender pay gaps exist [29, 30].

Engineering, industry, and mining careers demonstrate the highest wage returns, with these fields consistently showing strong demand in the labour market due to Peru’s economic reliance on its natural resources and industrial sectors. Conversely, education careers exhibit the lowest returns, reflecting both the challenges in the teaching profession and the historical undervaluation of educational roles in comparison to more technical fields [34, 35].

The positive impact of educational quality on wage returns is further supported by rankings like SCIMAGO, which assess academic institutions on research output and innovation. Graduates from higher-ranked institutions tend to secure better-paying jobs, suggesting that the prestige and quality of an educational institution play a significant role in labour market outcomes [41]. This pattern reflects global trends where higher-ranking institutions provide graduates with access to better networks, resources, and opportunities, thereby increasing their income potential.

Moreover, stark regional and urban disparities in wage returns remain evident. In Peru, Lima and other major urban centres offer significantly higher wage returns compared to rural areas. Urban areas benefit from more diverse and robust economies, which provide greater job opportunities and better remuneration. In contrast, rural areas tend to be less economically developed, offering fewer high-paying jobs, which exacerbates inequality in wage outcomes for graduates in these regions [9, 32]. This disparity highlights the need for targeted regional development policies to ensure more equitable distribution of economic opportunities and to reduce the income gap between urban and rural graduates.

The Beca 18 programme has improved access to higher education for vulnerable populations but faces challenges in student retention and programme’s cultural adaptation [42, 43]. While the programme demonstrates good coverage results, there is a significant loss of scholarships during the initial years of study, indicating a need for retention strategies throughout the university career [44, 45].

The Beca 18 programme has significantly improved access to higher education for vulnerable populations, offering opportunities to students from low-income backgrounds and marginalised communities. However, like similar programmes in Latin America, it faces challenges in ensuring student retention and cultural adaptation. Research on other scholarship initiatives in the region reveals comparable issues, where students often struggle to adjust to academic environments that may not cater to their specific cultural and socioeconomic contexts. For example, in Chile, the Gratuidad programme, which aims to provide free higher education, has encountered similar obstacles in retention, particularly among indigenous and rural students, who often lack adequate academic preparation and face significant cultural differences in urban universities [46].

In addition to retention, a major challenge across the region is the need for continuous support throughout students’ university careers. In Brazil, the Programa Universidade para Todos (ProUni) offers scholarships to low-income students, but retention rates are significantly impacted by insufficient academic and socio-emotional support [47]. A similar trend is observed in Argentina’s Programa Nacional de Becas Universitarias (PNBU), where dropout rates remain high despite financial aid, pointing to the need for additional mentoring and counselling services to help students navigate university life [48].

In the case of Beca 18, while the programme demonstrates good coverage results, there is a notable loss of scholarships during the initial years of study, reflecting a broader issue in the region where access to higher education is not enough to guarantee success. This underscores the importance of implementing comprehensive retention strategies that go beyond financial assistance to address the academic, psychological, and cultural needs of students. Studies from Mexico, where the Becas Benito Juárez programme supports low-income students, suggest that integrating mentorship and tutoring services significantly improves retention rates by providing ongoing academic support and fostering a sense of belonging [49].

The high dropout rates seen in Beca 18 are likely linked to a combination of academic underpreparedness, cultural dissonance, and the financial pressures that students face despite receiving scholarships. Many recipients, particularly those from rural and indigenous communities, struggle with the transition to university life in urban areas, where they often encounter social isolation and unfamiliar academic environments. These challenges are not unique to Peru but are part of a wider regional pattern. For instance, in Colombia, the Ser Pilo Paga programme has faced similar difficulties, with many students dropping out during the first year due to inadequate support systems [50].

To enhance the effectiveness of Beca 18 and similar programmes across Latin America, it is crucial to develop comprehensive retention policies that include academic advising, psychological support, peer mentorship, and cultural integration initiatives. Successful models from programmes like Ecuador’s Beca de Excelencia suggest that creating academic bridges, such as preparatory courses and tutoring, can significantly reduce dropout rates and improve student outcomes [51]. Moreover, a focus on soft skills development and social integration can help students better adapt to the demands of higher education, as seen in Uruguay’s Fondo de Solidaridad programme, which provides additional academic and emotional support to scholarship recipients [52].

An analysis of the characteristics of scholarship recipients and their impact on scholarship loss reveals key factors that influence the probability of dropout. One critical factor is the age at which the scholarship was received, with older students often facing additional personal and financial responsibilities that increase the likelihood of dropout. This pattern is not unique to Peru; similar trends have been observed in other Latin American countries where non-traditional students, particularly those who enter higher education later in life, face barriers to academic success due to balancing work, family, and studies [53].

The place of origin also plays a crucial role. Students from rural areas face unique challenges, including limited access to preparatory education, socioeconomic barriers, and the difficulty of transitioning from rural to urban life. In Peru, these students often experience social isolation and cultural dissonance in university settings, which contributes to higher dropout rates. A study in Bolivia similarly found that rural students in higher education tend to experience greater academic and social difficulties than their urban counterparts, underscoring the need for targeted support systems [54]. This suggests that programmes like Beca 18 should adopt more culturally sensitive approaches, particularly for students from indigenous and rural communities, who may have non-dominant native languages and face additional linguistic and cultural barriers.

Indeed, language plays a significant role in educational outcomes. Students whose native languages differ from the dominant language of instruction often struggle to adapt, leading to lower academic performance and higher dropout rates. In the context of Beca 18, students from indigenous communities who speak Quechua or Aymara, for example, may find it difficult to navigate the Spanish-dominant university environment. Similar challenges have been documented in Guatemala, where indigenous students with Mayan languages as their mother tongue experience higher dropout rates due to language barriers [55]. Addressing these challenges requires implementing language support programmes, bilingual education strategies, and initiatives that promote the inclusion of indigenous perspectives within university curricula.

Additionally, differences in scholarship loss probability by field of study and type of university indicate that certain disciplines and institutions may require more tailored academic and socio-emotional support. For instance, students enrolled in STEM (Science, Technology, Engineering, and Mathematics) fields often face more rigorous academic demands, which may increase the likelihood of dropout for those lacking a strong educational foundation. Similarly, students attending private universities tend to experience lower scholarship loss rates compared to those in public universities, which could reflect differences in resources, academic support, and student services between these institutions. This trend aligns with research from Mexico, where students in STEM fields and public universities encounter higher dropout rates due to the intensity of their studies and limited access to academic support [56].

Overall, these findings suggest that scholarship programmes need to be more flexible and adaptive to the diverse needs of recipients, considering factors, such as age, place of origin, language, field of study, and institutional characteristics. By providing more personalised support services—such as tutoring, counselling, and culturally relevant mentorship—programmes like Beca 18 can help reduce the risk of scholarship loss and improve retention outcomes. Lessons can be drawn from Ecuador’s Beca de Excelencia programme, which incorporates mentorship and career guidance to support students from diverse backgrounds, leading to higher retention rates and improved academic performance [51].

5. Conclusions and recommendations