Estimating the Returns to Education Using a Machine Learning Approach – Evidence for Different Regions

2.1 Rates of Returns to Education in Africa

After the tremendous work conducted by Psacharopoulos and Patrinos (2018), which extensively examined the global literature encompassing 1,120 estimates across 139 countries from 2004 to 2011, it was revealed that Sub-Saharan Africa exhibits one of the highest private returns to education (10.5%). These estimates were predominantly derived using the Mincer equation and the OLS algorithm. Montenegro and Patrinos (2014), after compiling data on the global returns to education from 139 economies spanning the years 1980–2013, discovered that Sub-Saharan Africa had the highest rates of returns (12.5%).

Depken, Chiseni, and Ita (2019) employed OLS and examined two waves of the National Income Dynamics Study to estimate the private returns to education in South Africa for the years 2010 and 2012. The analysis revealed returns of approximately 18% per year, with higher returns observed for females compared to males. Similar estimates were obtained by Salisbury (2015) using data from the 2008 wave. By employing the Two-Stage Least Squares (2SLS) procedure on the 2012 wave, Biyase and Zwane (2015) reported a return rate of 47%.

Moreover, multiple studies have focused on estimating the rates of returns to education in Tanzania. The most recent estimate, compiled by Montenegro and Patrinos (2014), indicates a rate of 16.1% in 2011. Returns on higher education were found to be the highest (19.4%), while secondary and primary education yielded similar returns (15 and 14.6%, respectively). In addition, Peet, Fink, and Fawzi (2015) estimated the return rate for Tanzania in 2010 to be 11.1%, while Serneels, Beegle, and Dillon (2017) reported estimated returns of 8% for men and 10% for women in 2008. Further rates of returns for both countries are provided in Table A1.

2.2 Rates of Returns to Education in the USA

In the USA, there is a consistent effort to estimate the returns on investment in education. The Organisation for Economic and Comparable Development (OECD) regularly publishes an article called “Education at a Glance,” which provides ongoing information about the state of education worldwide. These articles present data on education system finances, performance, and incentives to invest in education across OECD countries. Using data from the Programme for the International Assessment of Adult Skills (PIAAC) in 2016, OECD (2019) estimated the private internal rate of returns to tertiary education for both men and women in the USA to be 20%. This estimate was 18% in 2015. Furthermore, Fogg et al. (2018) found that in 2011–2012, high school dropouts were projected to earn 16–17% less than high school graduates. Conversely, bachelor’s degree holders earned 30% more than high school graduates, and master’s degree holders earned approximately 45% more than high school graduates. For additional estimates, please refer to Table A1.

2.3 Rates of Returns to Education in Asia

Significant research efforts have been undertaken to estimate the rates of returns to education in India and South Korea over the past decade. In the case of India, Sikdar et al. (2019) conducted the most recent study using data from 2011–2012. Employing the Mincerian equation, the study found insignificant relationships between wages and education level, estimating the financial returns to education at 1.5%. However, graduates from tertiary education typically obtain higher-paying jobs. Jacob et al. (2018), analyzing returns to various professions and degrees based on data from 2011–2012, found that medical graduates had the highest returns, followed by engineering graduates and professional postgraduates. Two additional studies (Agrawal, 2011; Rani, 2014), although based on older data from 2005, obtained rates of returns to education of 14 and 8.5%, respectively. Agrawal (2011) further examined the temporal change in returns to different levels of education and observed rising returns with higher education levels.

Turning to South Korea, estimates of the returns to education are primarily evaluated by the OECD using PIAAC data and the full discounting method. In 2016, private returns to tertiary education were estimated to be 22% for men, 20% for women, and 21% on average (OECD, 2019). Similarly, in 2015, returns to tertiary education averaged 22%, with men experiencing a rate of 25% and women 19% (OECD, 2018). As shown in Table A1, returns to education have increased from 2011 to 2016. Another study examining the Korean minority living in China, based on data from 2009–2010, revealed high returns to education compared to the Asian and world averages. This explains the strong private demand for education among the ethnic Koreans in China (Mishra & Smyth, 2013).

2.4 Rates of Returns to Education in Australia

The rates of returns to education in Australia are primarily investigated and reported by the OECD. In 2016, the average returns to higher education were estimated to be 13.5%, showing similar values for both men and women (OECD, 2019). There was only a marginal change compared to the previous year’s returns, which stood at 12% in 2018 (OECD, 2018). Additional estimates from the OECD can be found in Table A1.

Other studies have examined the returns to education in Australia using the Mincerian equation. Montenegro and Patrinos (2014) obtained a rate of returns to education of 14.1% for the year 2010, while Mariotti and Meinecke (2011) estimated a rate of 8.3% for the same year. The disparity in these findings can be attributed to the utilization of different datasets in each analysis.

2.5 Rates of Returns to Education in Europe

European countries, particularly those in Western Europe, are characterized by relatively low average rates of returns to education, as reported by Psacharopoulos and Patrinos (2018) with a value of 7.3%. In the case of Germany and Switzerland, returns to education have been consistently studied and analyzed.

According to the OECD reports, the rates of returns to tertiary education in Germany were 15% in 2016 (OECD, 2019) and 11% in 2015 (OECD, 2018). Using the Mincerian earning function with OLS estimation, Montenegro and Patrinos (2014) calculated rates of returns for the years 2010, 2011, and 2012, resulting in estimates of 15.2, 14.3, and 14.5%, respectively. Similarly, employing a two-stage least squares (2SLS) approach, Mysíková and Večerník (2015) obtained estimates of 15.6% in 2010 and 15.5% in 2011.

For Switzerland, calculations revealed comparable rates of returns to tertiary education in 2016 and 2015, both amounting to 14% (OECD, 2018, 2019). Previous calculations based on the Mincerian equation indicated higher returns, with estimates of 21% for the years 2010 and 2011 using the 2SLS method (Mysíková & Večerník, 2015). However, when employing OLS, the rates for 2011 and 2012 were approximately 12% (Montenegro & Patrinos, 2014).

The majority of studies included in this review rely on older data, with data collection concluding by 2012, except for those analyzed by the OECD, which includes the most recent data from 2016. It is important to note that the results provided by the OECD may have limitations due to the employed methodology, specifically the full discounting method. This approach is less commonly used in the economics literature, as most researchers typically adopt the standard and conventional approach based on the Mincer earnings function. In addition, studies based on the Mincer equation may occasionally fail to capture trends in different regions.

The present study focuses on examining the most recent data released in 2018 from diverse regions worldwide, including South Africa and Tanzania in Africa, the USA, South Korea and India in Asia, and Germany and Switzerland in Europe. These regions exhibit significant heterogeneity, and comparing their returns on educational investment can offer valuable insights. To enhance the accuracy and robustness of the estimation, an ML algorithm called SVR is employed in combination with cross-validation. This innovative approach allows for more reliable and precise estimations. The subsequent sections will provide detailed information about the empirical strategy and the data utilized for the analysis.

3.1 Mincerian Earning Function

Estimates of the returns to education are commonly derived using the Mincer model (Mincer, 1974; Patrinos, 2016). The Mincer model can be extended to estimate the returns to education at different levels of schooling. The standard Mincer model involves regressing the logarithm of hourly wage (or in some cases, weekly, monthly, or yearly wage) on a set of human capital variables. These human capital variables typically include total years of education, labor market experience, and experience squared. The Mincer equation can be expressed as follows:

where y _i = log(w _i ) denotes the natural logarithm of the earned wage for the individual i; S _i ∈ R constitutes the number of years of schooling; and X _i ∈ R represents the potential labor market experience. The labor market experience is calculated by subtracting from the age of the individual the number of years of schooling and the age at which the individual started schooling (X _i = Age _i − S _i − 6, with 6 being the average age for starting schooling). X _i ² is the square of the potential experience, and µ _i is the random error term reflecting unobserved characteristics. Consequently, the coefficient of interest β ₁ on years of schooling (S _i ) can be interpreted as the private average rate of return to an additional year of education. The basic Mincer function assumes that the rate of return is the same for all levels of schooling and considers foregone earnings as the sole costs of education. The inclusion of the experience variable in the equation accounts for the expected positive relationship between earnings and experience. In addition, the experience squared term captures the potential non-linear relationship between earnings and experience.

To analyze the returns to different levels of education, an extended version of the Mincer model can be employed. In this extended model, the continuous variable representing the total years of education (S) is transformed into a series of dummy variables: E _p for primary education, E _s for secondary education, and E _t for tertiary education. The extended Mincer equation can be expressed as follows:

In the extended Mincer equation, the dummy variables E _p, E _s, and E _t take a value of 1 when an individual has attained the respective level of education (primary, secondary, or tertiary). The reference level is represented by individuals with no education, and it is excluded from equation (2) to prevent matrix singularity.

Once the extended earning function has been computed, the private rates of return to each year of education for a specific level can be derived using the following approach:

with rate_p, rate_s, rate_t being the private rate of returns to primary, secondary, and tertiary education, respectively. S _p, S _s, and S _t stand for the average number of years spent in primary, secondary, and tertiary education. For convenience, 3 years are assigned for primary education, 6 years for secondary education, and 4–5 years for tertiary education.^[1] The specific duration of tertiary education may vary depending on the higher education system of each country.^[2]

The Mincer equation continues to be one of the most widely used models in econometrics for estimating returns to education. As highlighted by Montenegro and Patrinos (2014) and Patrinos (2016), estimates of returns to education derived from the Mincer equation exhibit stability and comparability. It is important to note that these estimates do not necessarily establish causality between education and earnings but rather indicate the conditional association between years of education and labor market incomes.

3.2 Machine learning (ML) in Econometrics

ML, and artificial intelligence (AI) more broadly, refers to the field of study focused on computer algorithms that automatically learn and improve knowledge through experience. ML has found numerous applications in econometrics, including computational finance, higher education, and health. ML algorithms possess the capability to identify natural patterns and intricate structures within large datasets, often outperforming traditional algorithms. They have emerged as transformative technologies with immense potential in higher education. These technologies are being utilized in various aspects of the education system, such as personalized learning, intelligent tutoring systems, academic analytics, and administrative tasks. By harnessing the power of ML and AI, higher education institutions and educators can enhance student engagement, improve learning outcomes, optimize resource allocation, and drive innovation in teaching and research methodologies. The application of ML and AI in higher education is a topic of increasing interest and has the potential to revolutionize the educational landscape (Bozkurt, Karadeniz, Baneres, Guerrero-Roldán, & Rodríguez, 2021; Zawacki-Richter, Marín, Bond, & Gouverneur, 2019).

In econometrics, traditional statistical algorithms are typically employed to estimate the parameters β that characterize the relationship between an output variable y and a set of input covariates x. These algorithms aim to identify the underlying statistical associations and make inferences about the relationship between the variables. Traditional statistical algorithms are commonly used for such inference tasks. However, recent discussions by Mullainathan and Spiess (2017) have highlighted the application of ML algorithms in econometrics. These ML algorithms, such as least absolute shrinkage and selection operator regression and SVR as utilized in this article, offer alternative approaches to modeling and prediction. ML algorithms can provide more flexibility by handling complex relationships and capturing nonlinearities that may be challenging for traditional statistical algorithms.

3.3 SVR

SVR is a widely used ML model for regression tasks and has found applications in various fields, including time series analysis, financial prediction, and engineering analyses. SVR builds upon certain aspects of traditional linear regression but introduces some key differences in its approach. While linear regression aims to minimize the sum of squared errors between the predicted and actual values, SVR focuses on minimizing the margin violation of a defined ϵ-insensitive zone around the true values. For a more comprehensive understanding of how SVR works and its underlying techniques, a detailed explanation and graphical illustrations can be found in references such as Bishop (2006) and Smola and Schölkopf (2004). These references delve into the mathematical foundations and algorithms behind SVR, providing a deeper insight into its methodology and applications.

Given a dataset with n observations (x ₁ ,y ₁), …, (x _n ,y _n ), x _i ∈ R ^d , and y _i ∈ R, the aim is to find a regression hyperplane

which leads to minimal deviation from the observed values y and, at the same time, is as flat as possible. Flatness means that small β are sought. The error function is then given as follows:

Instead of minimizing the quadratic error function, in which each deviation from the real outcome is penalized, SVR allows sparse solutions with at most ϵ deviation (ϵ > 0). Thus, the quadratic error function is replaced by an ϵ-insensitive error function (Cortes & Vapnik, 1995), in which the absolute difference between the prediction and the real outcome less than ϵ is not considered an error. The ϵ-insensitive function, as illustrated in Figure 1, is given as follows:

Figure 1

ϵ-Insensitive error function (red) with the error increasing linearly vs quadratic error function (green).

To deal with the points, for which | f ( x i ) − y i | > ϵ , two slack variables ξ ≥ 0 and ξ ˆ ≥ 0 are introduced. Thus, as illustrated in Figure 2, ξ _i > 0 corresponds to a point for which y _i > f(x _i ) + ϵ and ξ ˆ i > 0 corresponds to a point for which y _i < f(x _i ) − ϵ. Points lying inside the ϵ-tube correspond to f(x _i ) − ϵ ≤ y _i ≤ f(x _i ) + ϵ and have ξ i = ξ ˆ i = 0 . Therefore, the optimization problem to solve in ϵ-SVR has the following form:

Figure 2

ϵ-SVR with the ϵ-insensitive tube and slack variables ξ and ξ ˆ .

min C ∑ i = 1 n ( ξ i + ξ ˆ i ) + 1 2 ‖ B ‖ 2 subject to y i − x T B − β 0 ≤ ϵ + ξ i x T B + β 0 − y i ≤ ϵ + ξ ˆ i ξ i , ξ ˆ i ≥ 0 .

The constant C > 0 determines the trade-off between the flatness of the function f and the amount up to which deviations larger than ϵ are tolerated.^[5] To solve the optimization problem, its dual formulation with the help of the Lagrange function is used (for more details, see Bishop, 2006).

4.1 National Income Dynamics Study (NIDS) Data

The NIDS is a significant household panel study in South Africa, recognized as the first nationally representative and longitudinal study of its kind (Brophy et al., 2018). Since its initiation in 2008, NIDS has been conducting regular interviews and tracking over 28,000 individuals in 7,300 households across the country. The dataset encompasses five waves, specifically from the years 2008, 2010, 2012, 2015, and 2017. For this study, the focus is on the four most recent waves, which align with the timeframe of 2010–2019. A balanced distribution between men and women is observed across the different datasets, with women generally being slightly older than men. The average years of schooling amount to approximately 11.5 years, and women tend to have higher educational attainment compared to men. In 2017, the average hourly earnings were recorded as 48.3 South African Rands, representing a 31% increase compared to 2010. However, a gender pay gap of 24.3% in favor of men was observed in that year. Moreover, there exists a positive relationship between earnings and age, indicating that hourly earnings tend to increase with age. Individuals with higher education (38.0% of the sample) earned above the average earnings, while those with lower education earned below the average.

4.2 National Panel Survey (NPS) Data

The Tanzania NPS is a nationally representative household panel survey designed to collect information on the living standards of the Tanzanian population (National Bureau of Statistics, 2016). The NPS has been conducted since 2008 and consists of four waves (2009, 2011, 2013, and 2015). The survey includes approximately 5,000 households and around 10,000 individuals living within those households, who are interviewed and tracked regularly over the years. For this analysis, data from three waves (2011, 2013, and 2015) are used.

In the dataset, men are overrepresented, accounting for 65% of the data, while women comprise the remaining 35%. On average, both men and women have the same age and an equal number of years of education, specifically 8 years. Between 2011 and 2015, the mean hourly earnings increased from 1,356 to 1,595 Tanzanian Shillings, representing a growth of 15%. In 2015, the gender pay gap was approximately 25.3%, indicating a disparity in earnings favoring men. It is noteworthy that individuals with secondary education or lower attained similar average earnings, while those with higher education (7.3% of the sample) earned above the average.

4.3 Panel Study of Income Dynamics (PSID) Data

The PSID is a nationally representative longitudinal study focused on families and individuals in the USA. It was initiated in 1968 and has since continued to interview the same families and their descendants over nearly five decades, spanning from 1968 to 2017 (PSID, 2019). For this analysis, data covering the period from 2010 to 2017 are used, encompassing four waves (2011, 2013, 2015, and 2017). Each wave of data collection involves approximately 25,000 individuals from 9,000 households. In the PSID dataset, men and women are equally distributed and span various age groups. The average number of years of education remains relatively constant over the years, at approximately 14 years. Females tend to have slightly higher levels of education compared to males. In 2017, the average hourly earnings in the USA were 27.2 USD, representing a growth of about 4% compared to 2011. In addition, the average hourly earnings for men in 2017 were approximately 30.7 USD, which is on average 22% higher than women’s earnings. Examining the relationship between age and hourly earnings reveals a positive association, as hourly earnings tend to increase with age and stabilize around the age of 40. Individuals with higher education (comprising 65.6% of the sample) have the highest and above-average hourly earnings, while those with no education attain the lowest hourly earnings.

4.4 India Human Development Survey (IHDS) Data

The IHDS is a nationally representative survey that covers a wide range of topics. It initially collected data during the period of 2004–2005, and then individuals from the same households were re-interviewed in 2011–2012 (Desai & Vanneman, 2018). For this analysis, we focus specifically on the data from 2012. In the IHDS dataset, men constitute 70% of the sample, while women make up 30%. On average, both men and women have an age of 38 years. The average years of schooling amount to approximately 6 years, with men having higher levels of education compared to women. In 2012, the average hourly earning was around 28.2 Rupees. Men, on average, earned about 37.5% more than women. There is a positive relationship between earnings and age, indicating that earnings tend to increase with age. Individuals with higher education (10% of the sample) have the highest earnings, well above the average, while those with secondary education earn at or below the average level.

4.5 Korean Labor and Income Panel Study (KLIPS) Data

The KLIPS is a comprehensive labor-related panel survey that combines cross-sectional and time-series data in Korea (KLIPS, 2020). The survey is conducted annually, with a sample of approximately 5,000 urban households and 13,000 individuals. The survey has been conducted since 1998, with the latest wave (21) completed in 2018. For this article, the focus is on the data from the last decade (2010–2018). Each year’s data consist of approximately 60% men and 40% women, with both groups being roughly the same age, averaging 45 years. The average years of education show a slight increase over the years, from 12.7 in 2010 to 13.5 in 2018, with men generally having slightly higher levels of education than women. Over the years, the average hourly earnings in Korea have shown an increase of approximately 30%, rising from 11,165 Korean Won in 2010 to 15,919 Korean Won in 2018. However, there remains a gender pay gap of approximately 32%, favoring men. Individuals with higher education (accounting for 56.4% of the sample) have the highest earnings, indicating the importance of educational attainment in earning potential.

4.6 Household, Income and Labour Dynamics in Australia (HILDA) Data

The HILDA survey is a nationally representative longitudinal study that focuses on Australian households (Summerfield et al., 2019). The survey began in 2001 and currently consists of 18 waves of data, with the 18th release covering the period from 2001 to 2018 (waves 1–18). The dataset includes approximately 20,000 individuals from 9,000 households. For this analysis, the focus is on the data from the last decade (2010–2018). Men and women are equally distributed within the datasets, and their average age is approximately 39.5 years. The level of education is consistent for both men and women, amounting to about 13 years of education. Between 2010 and 2018, the average hourly earnings in Australia increased by 14.2%, rising from 33.8 USD to 39.4 Australian Dollars. In 2018, Australia’s gender pay gap was reported to be 9.7%. In addition, there is a positive relationship between age and hourly earnings, with earnings tending to increase with age and stabilizing around the age of 40. Individuals with higher education (accounting for 45.7% of the sample) are the highest earners, surpassing the average earnings. On the other hand, individuals with secondary or primary education levels tend to earn below the average.

4.7 Socio-Economic Panel (SOEP) Data

The German SOEP is a significant and long-running multidisciplinary household survey that includes approximately 15,000 households and 30,000 individuals (Markus, 2019). The survey has been conducted since 1984 and continues to interview many of the same families and individuals, with the latest data released in 2017. For this analysis, the focus is on data covering the period from 2010 to 2017. Men and women are equally represented within the datasets and have similar age distributions. The average schooling level is approximately 13 years of education. In 2017, the average hourly earning in Germany was around 20.9 Euros, reflecting an increase of 6.2% compared to 2010. On average, men earned about 18.6% more than women in 2017. There is a positive relationship between age and hourly earnings, indicating that earnings tend to increase with age. Individuals with higher education (representing 44.3% of the sample) have the highest earnings, surpassing the average level. Conversely, individuals with secondary education tend to earn below the average level.

4.8 Swiss Household Panel (SHP) Data

The SHP is a large-scale, nationally representative longitudinal study conducted in Switzerland since 1999, collecting data on households and individuals (Voorpostel et al., 2020). The SHP dataset currently consists of 20 waves, covering the period from 1999 to 2018. Each year, approximately 12,000 individuals from 5,000 households are surveyed. For this analysis, the focus is on data from the last decade (2010–2018). The data in the SHP are equally distributed between men and women, with similar age distributions. The average years of education show a slight increase over the years, rising from 14.1 in 2010 to 14.7 in 2018. Men generally have slightly higher levels of education compared to women. In 2018, the average hourly earning in Switzerland was approximately 45.9 Swiss Francs, reflecting a growth of 5% compared to 2010. On average, men earned about 18% more than women in terms of hourly earnings. There is a positive relationship between age and hourly earnings, indicating that earnings tend to increase with age. Individuals with higher education (comprising 59.6% of the sample) earned above the average and more than individuals with lower levels of education.

4.9 Patterns of Education and Earnings Across Regions

The analysis of education levels across different regions reveals interesting patterns. European countries, Australia, South Korea, and the USA have the highest levels of education, as indicated in Table 2. This finding aligns with previous predictions made by the International Institute for Applied Systems Analysis (IIASA) (IIASA, 2015). On the other hand, India has the lowest level of education, with 30.7% of its population lacking formal education. This observation supports IIASA’s predictions as well. A surprising finding is that Tanzania has a very low percentage (0.2%) of its population with no education, in contrast to the approximately 20% predicted by the IIASA. This discrepancy may reflect improvements in educational access and policies in Tanzania. Countries such as the USA, South Korea, and Switzerland have high proportions of their populations attaining higher education, with rates exceeding 50%. In addition, there is a gender gap favoring women in terms of educational attainment and the population with higher education. This trend is observed in regions such as Africa, the USA, and Europe, which aligns with findings from the OECD (2019). However, it is important to note that this gender gap is generally not observed in Asian countries. These findings shed light on the variation in education levels and gender gaps across different regions, highlighting the importance of considering regional differences in educational attainment and gender disparities in higher education.

The gender pay gap, which reflects the difference in mean gross hourly earnings between men and women expressed as a percentage of men’s earnings, indicates gender inequality in hourly pay. Across regions, the gender pay gap tends to favor men, with women earning, on average, 23% less than men. In Europe, the gender pay gap is around 18%, which is consistent with the estimate of 14.8% across Europe in 2018 as reported by Eurostat (2020). Australia has the lowest gender pay gap at 9.7%, while Asia has the highest gender pay gap at an average of 35%. These findings align with the observations of the International Labour Organization.^[7] Even among individuals with higher education, the gender pay gap persists, with men earning 19% more than women. In addition, when comparing the pay gap between individuals with higher education and those with secondary education, significant differences are observed. In Africa and India, the pay gap between these two groups is around 60%, while in other countries, it is around 30%. These findings are consistent with the research conducted by Livanos and Nunez (2012), which also revealed wage inequalities between individuals with higher education and those with secondary education.

5.1 Returns to Education

The returns to an additional year of education for each country are summarized in Table 3. In the first row, the total private rate of returns to another year of schooling is reported to be 10.4% for the period from 2010 to 2018. This result surpasses other findings reported by Psacharopoulos and Patrinos (2018) (9%), Patrinos (2016) (9.7%), and Montenegro and Patrinos (2014) (10.1%). Globally, the returns to education are highest in Africa, with a rate of 17.8%, significantly above the average. Healthy returns are also observed in America, Asia, and Australia, with rates of 11.5, 10.7, and 9.8%, respectively. On the other hand, the lowest returns are found in Europe, at only 7.2%. These findings highlight the variations in the returns to education across different regions. Africa stands out with the highest returns, while Europe lags behind with the lowest returns. These results provide important insights into the economic value of education in different parts of the world.

5.1.3 Returns to Education Over Time

As displayed in Figure 4, the returns to education tend to decrease slightly over time, with a rate of approximately 0.1% per year. This finding suggests that the economic benefits individuals derive from investing in education may gradually decline over time. This observation is consistent with previous research studies (Montenegro & Patrinos, 2014; Patrinos, 2016; Peet et al., 2015; Psacharopoulos & Patrinos, 2018). In contrast to the declining returns to education, the average number of years of education has been increasing over time at a rate of approximately 0.16 years per year or 1% per year. This finding implies that individuals are investing more in their education and acquiring higher levels of schooling as time progresses. The simultaneous increase in the average number of years of education and the decline in the returns to education may indicate a shift in the labor market’s demand for specific skills and qualifications. It suggests that while education remains important, other factors such as technological advancements, changing labor market dynamics, and skill requirements may influence the returns to education.

Figure 4

Returns to education and years of education over the last decade.

5.1.4 Returns to Education and Average Years of Education

The regression analysis displayed in Figure 5 suggests that there is a negative relationship between the number of years of education and the returns to education. Specifically, the analysis indicates that for each additional year of education, the returns to education decrease by 1.4 percentage points. This finding supports the notion that education responds to price signals in the economy. As the level of education increases within a given economy, the supply of educated individuals also increases. This increased supply can lead to a decrease in the returns to education as there is a larger pool of individuals with similar levels of education competing for similar job opportunities. The result obtained in this analysis is consistent with the findings of Montenegro and Patrinos (2014), suggesting that the relationship between education and the returns to education is influenced by market dynamics and the supply of educated individuals.

Figure 5

Decrease of returns to education as education increases.

5.2 Comparison with OLS Returns

To compare the returns to education estimated by the SVR model and the OLS model, both models are applied to the same dataset using the same set of variables. The performance of each model is evaluated using the R ² and RMSE metrics. By conducting 200 cross-validated simulations, the models’ performance is assessed on multiple subsets of the data, ensuring robust and reliable estimates of the model’s performance. This approach helps account for the potential variation in the results that could occur due to different data partitions.

According to the results presented in Table 4, the returns to education estimated using the SVR model are generally higher compared to those estimated using the OLS model. This suggests that the SVR model captures additional nonlinearities or patterns in the data that are not captured by the linear OLS model. In terms of predictive performance, the SVR models consistently outperform the OLS models. The SVR models achieve higher R ² values, indicating that they explain a larger proportion of the variance in the returns to education. In addition, the SVR models have lower RMSE values, indicating that their predictions are closer to the actual observed values. These results suggest that the SVR model, being an ML algorithm, is better able to capture the complex relationships and patterns in the data, resulting in more accurate and robust estimates of the returns to education. The SVR model’s ability to handle nonlinearity and capture more nuanced patterns gives it an advantage over the traditional OLS model in this context. It is important to note that the performance of the SVR model may vary depending on the specific dataset and the choice of hyperparameters. Nonetheless, the results presented in Table 4 suggest that the SVR model provides more reliable and accurate estimates of the returns to education compared to the OLS model in this particular analysis.