This study explores non-education-related factors that influence a country's standing. In addition to examining the annual progression of global university standings, we analyze social and economic indicators. The insights gained from the data are then used to propose strategies for improving the Philippines' position on the global stage.
Recent data indicates a consistent decline in the global standings of Philippine universities. In the 2025 Times Higher Education (THE) World University Rankings, Ateneo de Manila University remained in the 1001–1200 bracket, while the University of the Philippines (UP) stayed in the 1201–1500 range. De La Salle University, University of Santo Tomas, Mapua University, and Mindanao State University-Iligan Institute of Technology were all placed in the 1501+ bracket. This positions Philippine universities behind many of their Southeast Asian counterparts.[1]
Furthermore, in the 2024 THE Asia University Rankings, no Philippine institution secured a spot in the top 100. Ateneo de Manila University, for instance, experienced a significant drop in its “research quality” score, from 97.0 to 40.6, highlighting challenges in research output and impact.[2][3]
A 2023 policy note by the Philippine Institute for Development Studies (PIDS) highlighted that, despite increased government investment in education, funding remains low compared to more developed peers. This underfunding has led to poor learning outcomes, with students underperforming in essential competencies. Notably, a World Bank report cited in the same study emphasized that nine out of ten Filipino students could not read or understand a simple text by age 10, and only one in five achieved minimum proficiency in reading and mathematics, based on the 2018 Programme for International Student Assessment (PISA) results.[4]
Moreover, the government’s focus on rapidly expanding tuition-free public schools without corresponding increases in quality inputs, oversight mechanisms, and teacher incentives has resulted in a massification of low-quality education. This approach has failed to ensure that students meet prescribed proficiency standards before advancing to higher education levels. The same 2023 PIDS policy note recommends shifting the focus from massifying low-quality education to enhancing students’ cognitive and non-cognitive competencies.[4]
To enhance the quality and global competitiveness of Philippine universities, it’s imperative to consider broader socioeconomic factors. Investments in research infrastructure, faculty development, and international collaborations can create an environment conducive to academic excellence. Additionally, policies that promote economic stability and social development can indirectly bolster educational outcomes by providing a supportive context for learning and innovation.[5]
Research indicates a strong correlation between a country’s economic indicators and the global rankings of its universities.
The stability and quality of a country’s governance structures also play a significant role in the performance of its universities.
We downloaded from web sources that offered data, already available in CSV or XLSX format.
The World Development Indicators provided by The World Bank is a compilation of relevant, high-quality, and internationally comparable statistics about global development. From the available indicators, we have selected 21 social and economic indicators with data from 2013 to 2023 on 217 countries to analyze how they relate to educational growth. More information on The World Bank data compilation and the World Development Indicators can be found in the links below.
The UniversityRankings website is a joint project run by the State Secretariat for Education, Research and Innovation and swissuniversities that provides a compilation of global university rankings from multiple sources. For our study, we have decided to work on the QS Top Universities global university rankings from 2015 to 2025. This dataset includes yearly university rankings, where the 2015 and 2016 rankings each contain 700 universities, the 2017 rankings contain 900 universities, and the 2018 to 2025 rankings each contain 1000 universities. More information on UniversityRankings, QS Top Universities and their data collection methodologies can be found in the links below.
First, country names between the two datasets are matched and countries with no matching data are removed, which brings the dataset down to 97 countries. Since the World Development Indicators are numerical (float) data and contain empty values, these null values are filled using linear interpolation into forward fill and backward fill. After interpolation, the remaining countries with null values are observed to not have a significant amount of entries in the rankings and are therefore removed, bringing the dataset down to 77 countries. Finally, indicators that change units, change values dramatically year to year, and have values that are not inherently bounded are normalized.
Since the 2015, 2016, and 2017 rankings contain less than 1000 universities each, the rankings are normalized into percentile. To represent a country’s education performance, the weighted mean of the percentile rankings of each country is taken, with higher rankings given more weight to preserve and emphasize the prestige of these universities.
Get an idea of the Philippines' ranks throughout the years by looking at the rankings and GDP (most common measurement of a country's economic performance).
The graph above shows the relationship between the Philippine university rankings and the country’s gross domestic product (GDP) from 2014 to 2024. The median percentile rank of the Philippines hovers around the 35th and 50th percentile with lowest being in 2015 and the highest in 2024. The Philippines’ GDP in current US dollars, however, shows a consistent upward trend throughout the decade. This shows a steady economic growth, excluding the dip in 2021 probably due to the COVID-19 pandemic. As seen, the economic growth does not appear to be directly proportional to the university rankings. This clearly implies that economic factors like GDP is not the only factor but many more can affect university standings.
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Looking at the Philippine university rankings and the country’s GDP in percentile form from 2014 to 2024, there is still no clear correlation between the two factors and may suggest that economic performance is not sufficient to affect university rankings. Economic growth does not directly translate into improved university performance. This starts a conversation that the Philippines’ poor economic performance can be slightly connected to the below average education performance. The Philippines has advanced economically throughout the years but what about its educational performance.
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The country who topped the rankings throughout the years was the United States with Massachusetts Institute of Technology - MIT being an outstandingly ranked university. The strong and stable economic performance of the United States is clearly seen along with the high education performance. This does not automatically imply that the strong economic performance directly translates to strong education performance. This gives us an insight that a strong economy can lead to a good educational system or vice versa. An indication that a highly developed economy like the United States can support world-class academic institutions.
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Examining Middle Economic Performers like Korea Republic, the same can be observed. The economic performance is better than the Philippine economy but weaker than the United States. The education performance is also seen as between the two countries. This is very interesting to consider as one can clearly see a connection between economic growth and education performance. However, it is important to keep in mind that there may be other factors to consider.
With each dot being a country, the plot can reveal some insights regarding the relationship between university ranking and GDP in percentile form. There is a large concentration of countries in the lower left, indicating the countries with a low GDP having varied university rankings. The countries with low economic performance until above average economic performance have varied university rankings. There are also countries with high education performance but weak GDP performance. This can be an important insight to investigate further, revealing that there is no clear connection between the two factors. The absence of dots on the lower right shows that countries with high GDP do not have low university rankings. These may be interesting but it is also important to consider other factors aside from economic growth.
The Philippines is highlighted as red and seen to be around the 20th percentile in GDP and below average university rankings. The multiple dots represent its performance per year from 2014 to 2024. Other countries like Korea Republic are also highlighted in the middle of the pack. It can be seen that even if there is a change in GDP throughout time, the country university ranking remains constant. The United States highlighted in blue is an example of a high-performing country in GDP having an outstanding country university ranking. Looking at the three countries, the variety of rankings can be seen relative to the GDP. The university rankings are more stable when the GDP is stable, as seen in the United States where they perform very well in GDP consistently thus having a consistent university ranking. However, the Philippines has a varied GDP every year which reflects also on the varied university rankings. Overall, the key to obtaining a better education system is to have a stable high GDP. Note however that there are other economic and social indicators that affect these rankings.
After the nutshell analysis, three main research questions were formulated together with their corresponding hypotheses.
Using Random Forest, importance scores of features can be computed. Only the features that are part of the 95% cumulative importance will be analyzed.
Hierarchical Clustering will be used to determine how many clusters best describe the groupings of countries based on similar features.
The difference of rankings among clusters can be measured by one of the following, depending on the distribution of the data:
The similarities of rankings within clusters can be measured by looking at how the data is described:
Three models were used to try and predict the percentile ranks of countries based on features.
The results of each model were compared with the others to see which model can best predict the rankings.
Here's what we found out.
The features that are clearly important relative to the other features are the Gross Domestic Product (GDP) , the Rule of Law and the Government Effectiveness of the country. These have a large impact on the country’s university rankings. These three features are all related to the government and how they handle the country. By looking at these factors, an assumption can already be made about the country’s ranking. It proves that the way a country is managed plays an important role in its educational system. One can argue that it makes sense that these economic and social factors have the most influence. Notable features like Scientific and Technical Journal Articles, Adolescent Fertility Rate and Labor Force Participation Rate can also slightly influence the rankings. However, features such as Access to Electricity, GDP growth, Foreign Direct Investment and etc. remain minimal to insignificant to the rankings. From this, features that are not within the 95% cumulative importance can be removed. Overall, the features regarding the performance of the government prove to be the most important factors that influence a country’s university rankings.
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The hierarchical clustering dendrogram shows that the countries are grouped into three clusters. The Philippines is part of the green clusters along with countries like Morocco, Romania and Costa Rica. This indicates that the Philippines have the most similar features to these countries compared to others in other clusters like the United States and Singapore.
The three main clusters namely Cluster 1 (yellow), Cluster 2 (green), and Cluster 3 (red) are the main clusters. The top indicators of each cluster was calculated by getting the deviation from the average of all of the clusters. For Cluster 1, the feature Merchandise Trade stands out as the main differential indicator. These are countries like Poland, Belarus, Portugal and Singapore. For Cluster 2, features like Adolescent Fertility Rate, GNI Per Capita and Merchandise Trade are the top indicators. These contain countries like the Philippines, Costa Rica, Peru and others. For Cluster 3, the GNI Per Capita and Merchandise Trade are the top indicators. These are countries like the United States, France and Italy. It is clear that the Merchandise Trade and GNI Per Capita are the main differences between each cluster. This gives us insights on how the countries of each cluster are similar in these factors and mainly grouped by this. Economic indicators stand out as the main differences between the clusters.
Using the Kruskal-Wallis test, it was found out that the distributions of the country university rank differ significantly between at least two clusters. The similarity of tank within clusters was then calculated using Coefficient of Variance. Cluster 1 contains the middle valued rankings with a CV of 50.54% indicating a high relative variability. The rankings are diverse with some outliers with higher-than-average values. Cluster 2 contains the lowest rankings among the three clusters. They have the highest relative variability but a spread-out distribution. This can indicate the disconnectedness between indicators and the rankings in Cluster 2. Lastly, Cluster 3 contains the highest rankings and lowest relative variability. This suggests a strong high-performing group.
Overall, this can suggest that the higher ranking countries (Cluster 3) are the most consistent while the lowest-performing countries (Cluster 2) are the most dispersed. Countries with similar values in features like Merchandise Trade and GNI Per Capita have been the main clusters. With Cluster 1 containing many first-world countries, it can be seen they are consistently ranked highly. The other clusters containing third-world countries show a dispersed distribution of rankings. This suggests that high-performing countries are consistently ranked higher and have similar rankings with countries like them. On the other hand, the low-performing countries with similar indicator values have varied rankings despite their similarity in indicator value.
The graphs show the difference with the 3 models used for the study. The results show that among the models Linear regression showed a poor R2 score of 0.62 while comparing it to MLP Regressor and Random Forest. This indicates that it explains only 62% of the predictions while also showing significantly higher error rates with an MSE,MAE, and RMSE compared to MLP Reressor and Random Forest which indicates that linear relationships alone are not accurate to capture the factors that influence university rankings. These numbers also show that university rankings follow mostly non-linear patterns that require more modeling approaches to capture data more precisely.
The high R2 scores achieved by both MLP Regressor and Random Forest shows strong implications that university rankings can be predicted using economic and social indicators.This aligns with the analysis from RQ1 that GDP,Rule of Law and the Government Effectiveness of the country have the most influential factors. Due to the high R2 results it suggests that the relationship between economic and social factors with educational performance follows predictable patterns. The better results from MLP Regressor and Random Forest over linear regression model shows the complex nature of factors influencing university rankings. This aligns with the analysis from RQ2 that similar indicator values have varied rankings despite their similarity in indicator value, particularly in lowest-performing clusters. The MLP and Random Forest model better understands and sees these relationships between variables.
Overall, the results give useful insights for understanding the Philippines’ declining rankings. High accuracy models suggest that improvements in the most influential factors may lead to predictable enhancements in university performance.
The study provided insights about the relationship between a country’s global university ranking and the country’s economic and social situation. Realizing that the Philippines has an underperforming education system, it is a wake-up call to promote quality and effective education. High-performing economy and stable society countries become a role model for obtaining educational success. Through more analysis, it was concluded that the global university rankings were primarily related to economic indicators like Gross Domestic Product (GDP) and government performance like the Rule of Law and the Government Effectiveness. This shows that how the country is managed plays a vital role in affecting its educational sector. Countries with similar indicators also have similar rankings like the United States. The high-performing countries are the most consistent in the global university rankings as opposed to low-performing countries that have more dispersed rankings. This gives insights that high-performing countries have similar rankings while low-performing countries have scattered rankings.
The study also used machine learning models (Linear Regression, MLP Regressor, Random Forest) to predict the rankings of a country given its social and economic indicators. The random forest model provided the most accurate results as it can handle the interactiveness of the data and its threshold effects. The rankings were able to be predicted with a R^2 score of 92%.
Overall, in order to address the education problem of the Philippines, the solution shouldn’t just be to improve the educational sector but to also improve the country’s socioeconomic status. The country should focus on creating policies and programs to strengthen our social and economic sectors. A strong management of the country can be the key to improving the Philippine’s position in the global stage. Learning from high-performing countries, the Philippines has the ability to catch up, by focusing on improving the socioeconomic factors of the country.
Hi. We are GRACE from WFW-Summer, and here is our Data Science Team and SDG