tibble [25,781 × 13] (S3: tbl_df/tbl/data.frame)
$ Prom : num [1:25781] 2019 2011 2008 2025 2013 ...
$ Nombre : chr [1:25781] "Alejandro Abad Vélez Abad" "Alejandro Abad Vélez Abad" "María Isabel Abad Londoño Abad" "Álvaro Leonardo Abadía Rincón Abadía" ...
$ Género : chr [1:25781] "Masculino" "Masculino" "Femenino" "Masculino" ...
$ Región Origen : chr [1:25781] "Antioquia" "Antioquia" "Distrito Capital" "Valle del Cauca" ...
$ Univ. Pregrado: chr [1:25781] "Escuela de Ingeniería de Antioquia" "Escuela de Ingeniería de Antioquia" "Universidad de los Andes" "Pontificia Universidad Javeriana (puj), Seccional Cali" ...
$ Pregrado : chr [1:25781] "Ingeniero Administrador" "Ingeniero Administrador" "Antropólogo" "Médico" ...
$ Univ. Posgrado: chr [1:25781] "London Business School" "Politecnico Di Torino" "Universidad Autónoma de Madrid" "Universidad Nacional Autónoma de México - Unam" ...
$ País : chr [1:25781] "Reino Unido" "Italia" "España" "México" ...
$ Ciudad Destino: chr [1:25781] "London" "Turín" "Madrid" "Ciudad de México" ...
$ Tipo : chr [1:25781] "Maestría" "Maestría" "Maestría" "Especialización" ...
$ Posgrado : chr [1:25781] "Mba" "Engineering Management" "Estudios Latinoamericanos" "Ortopedia" ...
$ Área : chr [1:25781] "Administración y Negocios" "Ingeniería" "Ciencias Políticas y Relaciones Internacionales" "Ciencias de la Salud" ...
$ Estado : chr [1:25781] "Beneficiario" "Beneficiario" "Beneficiario" "Seleccionado" ...Análisis colfuturo
1 Introduction
This document analyzes data on Colfuturo beneficiaries and selected applicants from 1992 to 2025. The goal is to study how the number of students has evolved over time, identify the main destination countries and universities, and examine the factors associated with the probability of becoming a beneficiary rather than remaining only a selected applicant.
The analysis focuses on comparing two groups: beneficiaries, who were awarded and used Colfuturo funding, and selected applicants, who were awarded the scholarship but did not use it. This distinction is important because it allows us to understand not only who is selected, but also which characteristics may be related to the actual use of the scholarship.
2 Libraries and Data Loading
3 Part 1 — Beneficiaries and Selected Applicants Over Time
3.1 Summary Table
# A tibble: 60 × 3
Prom Estado N
<dbl> <fct> <int>
1 1992 Beneficiario 46
2 1993 Beneficiario 52
3 1994 Beneficiario 51
4 1995 Beneficiario 48
5 1996 Beneficiario 84
6 1997 Beneficiario 169
7 1998 Beneficiario 137
8 1999 Beneficiario 103
9 2000 Beneficiario 75
10 2000 Seleccionado 20
# ℹ 50 more rowsThe summary table reports the number of observations by year and applicant status. The data show that the number of Colfuturo participants increased substantially over time. In the first years of the dataset, the number of beneficiaries was relatively low, with fewer than 200 observations per year. However, from the late 2000s onward, the number of participants increased strongly, which suggests an expansion of Colfuturo’s role in financing postgraduate education abroad.
The table also shows that selected applicants start appearing consistently after 2000. This distinction is important because it allows the analysis to compare students who actually used Colfuturo funding with those who were selected but did not use it.
3.2 Line Chart
The line chart shows a clear upward trend in the number of Colfuturo beneficiaries over time. Beneficiaries increased especially after 2006, reaching much higher levels during the 2010s and early 2020s. This suggests that the program expanded significantly and that more students were able to use Colfuturo funding to pursue postgraduate studies abroad.
The graph also shows that the number of selected applicants increased over time, but with more variation. A particularly important point is 2020, when selected applicants increased sharply and exceeded beneficiaries. This may reflect the effect of the COVID-19 pandemic, since many students could have been selected but were unable to start their programs because of travel restrictions, uncertainty, visa delays, or financial constraints.
After 2020, beneficiaries recovered and again became higher than selected applicants. This suggests that the use of Colfuturo funding improved after the pandemic shock. However, the fall observed in 2025 should be interpreted carefully, because the year may still be incomplete in the dataset.
3.3 Bar Chart
The bar chart complements the line chart by showing the total number of students in each year and how that total is divided between beneficiaries and selected applicants. The stacked bars make it clear that the total size of the program grew over time, especially after 2007.
In most years, beneficiaries represent the largest part of the total, which means that many selected students effectively used the scholarship or funding opportunity. However, the share of selected applicants also becomes more visible in recent years, especially around 2020. This reinforces the idea that being selected does not automatically imply becoming a beneficiary, since the final use of the scholarship may depend on additional academic, financial, or external conditions.
Overall, Part 1 shows that Colfuturo grew strongly between 1992 and 2025. The program moved from having a small number of beneficiaries in the 1990s to reaching much larger cohorts in the 2010s and 2020s. The main exception is 2020, where selected applicants increased sharply, probably because some students could not use the scholarship during the pandemic period. This first result suggests that the difference between being selected and becoming a beneficiary is relevant and should be analyzed in the next sections.
4 Part 2 — Descriptive Analysis
This section describes the main characteristics of the Colfuturo dataset. The analysis focuses on undergraduate universities, graduate universities, destination countries, program type, and area of study. The objective is to identify where students come from, where they go, and whether the data show concentration in specific institutions, countries, or academic fields.
4.1 Undergraduate Universities
The undergraduate university distribution shows a strong concentration among a small group of Colombian institutions. Universidad de los Andes appears as the most frequent undergraduate university in the dataset, followed by Universidad Nacional de Colombia and Pontificia Universidad Javeriana. This suggests that a large share of Colfuturo beneficiaries and selected applicants come from well-known universities with strong academic networks and greater exposure to international postgraduate opportunities.
After these three institutions, there is a second group that includes Universidad del Rosario, EAFIT, Universidad Externado de Colombia, Universidad del Norte, Universidad de Antioquia, Universidad Santo Tomás, Universidad de La Sabana, Universidad Industrial de Santander, Universidad Pontificia Bolivariana, Universidad del Valle, and Universidad Icesi. Although their frequencies are lower, they still represent an important part of the applicant pool. Overall, this pattern suggests that access to Colfuturo is not evenly distributed across all Colombian universities, but is concentrated in institutions with stronger academic and professional networks.
4.2 Graduate Universities
# A tibble: 15 × 2
`Univ. Posgrado` n
<chr> <int>
1 London School of Economics 861
2 Politecnico di Milano 828
3 University College London 761
4 Columbia University 584
5 New York University 528
6 Harvard University 423
7 University Of The Arts London 415
8 Vrije Universiteit Amsterdam - Vu 384
9 Monash University 327
10 Technische Universität München - Tum 315
11 University Of Melbourne 257
12 The University Of Queensland - Uq 255
13 Politecnico Di Torino 254
14 Georgetown University 240
15 University Of Oxford 197
The graduate university distribution shows that Colfuturo students are concentrated in internationally recognized institutions. The most frequent postgraduate universities include Politecnico di Milano, University College London, Harvard University, University of the Arts London, Columbia University, Vrije Universiteit Amsterdam, London School of Economics, Monash University, New York University, and Technische Universität München.
This pattern suggests that many students use Colfuturo to access universities with strong international reputation, especially in Europe, the United States, and Australia. However, this graph should be interpreted as a measure of frequency, not as a direct measure of university quality. A university appearing more often in the dataset does not necessarily mean that it is better; it means that more Colfuturo students attended or planned to attend that institution. To evaluate quality more precisely, it would be necessary to complement the dataset with external rankings or accreditation indicators.
4.3 Destination Countries
# A tibble: 15 × 2
País n
<fct> <int>
1 Estados Unidos 5723
2 Reino Unido 5623
3 Alemania 2474
4 España 2126
5 Australia 1908
6 Francia 1829
7 Países Bajos 1757
8 Italia 1426
9 Canadá 842
10 Suecia 338
11 Bélgica 272
12 Brasil 267
13 Suiza 205
14 México 169
15 Nueva Zelanda 91
The destination country distribution shows that international mobility among Colfuturo students is highly concentrated in a few countries. The United States and the United Kingdom are the two main destinations, with more than 5,000 students each. They are followed by Germany, Spain, Australia, France, the Netherlands, Italy, and Canada.
This concentration suggests that students tend to choose countries with strong higher education systems, internationally recognized universities, and a large supply of postgraduate programs. It also shows that Colfuturo mobility is not evenly distributed across the world, but mainly directed toward traditional education hubs in North America, Europe, and Australia. These destinations may be attractive because of academic reputation, language, labor market opportunities, and the availability of specialized master’s and doctoral programs.
5 Part 3 — Map of Destination Countries
The map shows the geographic concentration of Colfuturo destination countries. The strongest concentrations are located in North America and Europe, especially in the United States and the United Kingdom. This result is consistent with the descriptive analysis, where these two countries appeared as the main destinations for Colfuturo students.
Other important destinations include Germany, Spain, France, the Netherlands, Italy, Australia, and Canada. These countries are traditional centers of postgraduate education and have a large supply of internationally recognized universities and programs. The map also shows that Colfuturo students are not evenly distributed across the world; instead, they are mainly concentrated in countries with strong higher education systems, academic reputation, and greater international opportunities.
Overall, the map supports the idea that destination choice is strongly related to the global structure of higher education. Colfuturo students tend to move toward countries that offer prestigious universities, diverse postgraduate programs, and better professional or academic networks.
6 Part 4 — Logit Model
This section estimates a logit model to analyze which factors are associated with the probability of becoming a Colfuturo beneficiary rather than remaining only a selected applicant. The dependent variable is binary: it takes the value of 1 when the person is a beneficiary and 0 when the person is selected. The explanatory variables included in the model are gender, program type, destination region, and area of study.
A logit model is appropriate because the outcome variable has only two possible values. The coefficients are interpreted in terms of odds. For this reason, the model also reports odds ratios, which make the results easier to understand.
6.1 Model Estimation
Call:
glm(formula = y ~ Género + Tipo + Region + Área, family = binomial(link = "logit"),
data = df)
Coefficients:
Estimate
(Intercept) 0.4815822
GéneroMasculino 0.0176262
GéneroNo binario -0.9608767
TipoDoctorado en Administración 2.0284860
TipoEspecialización 1.3633821
TipoMaestría 0.3500761
TipoMaestría Administración 1.2063924
RegionNorteamérica 0.1605459
RegionOceanía -0.4407608
RegionOtro 0.0155527
ÁreaArquitectura y Diseño -0.2167142
ÁreaArtes 0.1988848
ÁreaC.agropecuarias 8.7342995
ÁreaCiencias Agropecuarias y del Medio Ambiente -0.0563986
ÁreaCiencias Básicas -0.3110982
ÁreaCiencias de la Salud -0.8083053
ÁreaCiencias Políticas y Relaciones Internacionales 0.4325893
ÁreaCiencias Sociales 0.2620042
ÁreaDerecho 0.6092472
ÁreaDerecho, Ciencias Políticas y Relaciones Internacionales -3.9568394
ÁreaEconomía 0.1689934
ÁreaEducación -0.1630269
ÁreaIngeniería 0.0004079
Std. Error z value
(Intercept) 0.0664279 7.250
GéneroMasculino 0.0288131 0.612
GéneroNo binario 0.4913118 -1.956
TipoDoctorado en Administración 1.0398437 1.951
TipoEspecialización 0.1629379 8.367
TipoMaestría 0.0432321 8.098
TipoMaestría Administración 0.0980537 12.303
RegionNorteamérica 0.0352578 4.553
RegionOceanía 0.0496828 -8.871
RegionOtro 0.0723111 0.215
ÁreaArquitectura y Diseño 0.0663680 -3.265
ÁreaArtes 0.0748257 2.658
ÁreaC.agropecuarias 72.4628721 0.121
ÁreaCiencias Agropecuarias y del Medio Ambiente 0.0738531 -0.764
ÁreaCiencias Básicas 0.0705166 -4.412
ÁreaCiencias de la Salud 0.0711937 -11.354
ÁreaCiencias Políticas y Relaciones Internacionales 0.0762009 5.677
ÁreaCiencias Sociales 0.0661246 3.962
ÁreaDerecho 0.0754648 8.073
ÁreaDerecho, Ciencias Políticas y Relaciones Internacionales 1.0286252 -3.847
ÁreaEconomía 0.0907796 1.862
ÁreaEducación 0.0800266 -2.037
ÁreaIngeniería 0.0566425 0.007
Pr(>|z|)
(Intercept) 4.18e-13 ***
GéneroMasculino 0.54071
GéneroNo binario 0.05050 .
TipoDoctorado en Administración 0.05109 .
TipoEspecialización < 2e-16 ***
TipoMaestría 5.61e-16 ***
TipoMaestría Administración < 2e-16 ***
RegionNorteamérica 5.28e-06 ***
RegionOceanía < 2e-16 ***
RegionOtro 0.82970
ÁreaArquitectura y Diseño 0.00109 **
ÁreaArtes 0.00786 **
ÁreaC.agropecuarias 0.90406
ÁreaCiencias Agropecuarias y del Medio Ambiente 0.44507
ÁreaCiencias Básicas 1.03e-05 ***
ÁreaCiencias de la Salud < 2e-16 ***
ÁreaCiencias Políticas y Relaciones Internacionales 1.37e-08 ***
ÁreaCiencias Sociales 7.42e-05 ***
ÁreaDerecho 6.84e-16 ***
ÁreaDerecho, Ciencias Políticas y Relaciones Internacionales 0.00012 ***
ÁreaEconomía 0.06266 .
ÁreaEducación 0.04163 *
ÁreaIngeniería 0.99425
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 31499 on 25778 degrees of freedom
Residual deviance: 30447 on 25756 degrees of freedom
(2 observations deleted due to missingness)
AIC: 30493
Number of Fisher Scoring iterations: 8 (Intercept)
1.618633e+00
GéneroMasculino
1.017782e+00
GéneroNo binario
3.825573e-01
TipoDoctorado en Administración
7.602568e+00
TipoEspecialización
3.909393e+00
TipoMaestría
1.419175e+00
TipoMaestría Administración
3.341409e+00
RegionNorteamérica
1.174152e+00
RegionOceanía
6.435466e-01
RegionOtro
1.015674e+00
ÁreaArquitectura y Diseño
8.051600e-01
ÁreaArtes
1.220041e+00
ÁreaC.agropecuarias
6.212381e+03
ÁreaCiencias Agropecuarias y del Medio Ambiente
9.451623e-01
ÁreaCiencias Básicas
7.326419e-01
ÁreaCiencias de la Salud
4.456126e-01
ÁreaCiencias Políticas y Relaciones Internacionales
1.541243e+00
ÁreaCiencias Sociales
1.299532e+00
ÁreaDerecho
1.839046e+00
ÁreaDerecho, Ciencias Políticas y Relaciones Internacionales
1.912346e-02
ÁreaEconomía
1.184112e+00
ÁreaEducación
8.495683e-01
ÁreaIngeniería
1.000408e+00 The logit results suggest that the probability of becoming a beneficiary is associated with program type, destination region, and area of study. In contrast, gender does not appear to be a strong predictor in this model, since the coefficient for male students is not statistically significant. This means that, after controlling for the other variables, there is no clear evidence that male and female students have different probabilities of becoming beneficiaries.
Program type appears to be an important factor. Categories such as master’s programs, management-related master’s programs, and specializations show positive and statistically significant coefficients. This suggests that, compared with the reference program category, these types of programs are associated with higher odds of becoming a beneficiary. One possible explanation is that these programs may be shorter, more standardized, and easier to finance than longer academic programs.
Destination region also matters. Compared with the reference region, North America has a positive and statistically significant coefficient, while Oceania has a negative and statistically significant coefficient. This suggests that students going to North America have higher odds of becoming beneficiaries, while students going to Oceania have lower odds. These differences may be related to program costs, visa conditions, distance, available funding, or the feasibility of actually starting the postgraduate program.
The area of study also shows relevant differences. Areas such as Law, Political Science and International Relations, Social Sciences, and Arts are associated with higher odds of becoming a beneficiary, while Health Sciences, Basic Sciences, Architecture and Design, and Education show lower odds compared with the reference area. This suggests that the probability of using Colfuturo funding is not the same across academic fields. Some areas may involve longer programs, higher costs, additional professional requirements, or different funding alternatives.
Overall, the model shows that becoming a beneficiary is not random. The transition from being selected to actually using Colfuturo funding depends on a combination of program characteristics, destination region, and field of study.
(Intercept)
1.618633e+00
GéneroMasculino
1.017782e+00
GéneroNo binario
3.825573e-01
TipoDoctorado en Administración
7.602568e+00
TipoEspecialización
3.909393e+00
TipoMaestría
1.419175e+00
TipoMaestría Administración
3.341409e+00
RegionNorteamérica
1.174152e+00
RegionOceanía
6.435466e-01
RegionOtro
1.015674e+00
ÁreaArquitectura y Diseño
8.051600e-01
ÁreaArtes
1.220041e+00
ÁreaC.agropecuarias
6.212381e+03
ÁreaCiencias Agropecuarias y del Medio Ambiente
9.451623e-01
ÁreaCiencias Básicas
7.326419e-01
ÁreaCiencias de la Salud
4.456126e-01
ÁreaCiencias Políticas y Relaciones Internacionales
1.541243e+00
ÁreaCiencias Sociales
1.299532e+00
ÁreaDerecho
1.839046e+00
ÁreaDerecho, Ciencias Políticas y Relaciones Internacionales
1.912346e-02
ÁreaEconomía
1.184112e+00
ÁreaEducación
8.495683e-01
ÁreaIngeniería
1.000408e+00 # A tibble: 23 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 0.482 0.0664 7.25 4.18e-13
2 GéneroMasculino 0.0176 0.0288 0.612 5.41e- 1
3 GéneroNo binario -0.961 0.491 -1.96 5.05e- 2
4 TipoDoctorado en Administración 2.03 1.04 1.95 5.11e- 2
5 TipoEspecialización 1.36 0.163 8.37 5.89e-17
6 TipoMaestría 0.350 0.0432 8.10 5.61e-16
7 TipoMaestría Administración 1.21 0.0981 12.3 8.69e-35
8 RegionNorteamérica 0.161 0.0353 4.55 5.28e- 6
9 RegionOceanía -0.441 0.0497 -8.87 7.22e-19
10 RegionOtro 0.0156 0.0723 0.215 8.30e- 1
# ℹ 13 more rowsThe odds ratios provide a more intuitive interpretation of the logit model. Values above 1 indicate higher odds of becoming a beneficiary compared with the reference category, while values below 1 indicate lower odds.
For example, the odds ratio for North America is above 1, which means that students going to this region have higher odds of becoming beneficiaries compared with the reference region. In contrast, the odds ratio for Oceania is below 1, suggesting lower odds of becoming a beneficiary. Similarly, program types such as master’s degrees, management-related master’s degrees, and specializations have odds ratios above 1, indicating a positive association with beneficiary status.
However, some very large odds ratios should be interpreted carefully. Extremely high values may appear when a category has very few observations or when almost all students in that category belong to the same status group. Therefore, the main interpretation should focus on variables with statistically significant and stable results.
In conclusion, the logit model provides evidence that the likelihood of becoming a Colfuturo beneficiary is related mainly to program type, destination region, and area of study. Gender does not show a statistically significant effect in this specification. The results suggest that being selected is only the first step, while the actual use of Colfuturo funding depends on academic, geographic, and program-related conditions.
7 Part 5 — Comparison Between Beneficiaries and Selected Applicants
This section compares beneficiaries and selected applicants across program type and graduate university groups. The objective is to identify whether both groups have similar characteristics or whether there are systematic differences in the type of programs and universities they attend. This comparison helps evaluate whether becoming a beneficiary is associated with specific academic choices.
7.1 Program Type
Beneficiario Seleccionado
Doctorado 1823 1111
Doctorado en Administración 13 1
Especialización 198 61
Maestría 14788 6355
Maestría Administración 1221 208The comparison by program type shows that master’s degrees are the dominant category for both beneficiaries and selected applicants. Most students in the dataset are enrolled in regular master’s programs, followed by doctoral programs and management-related master’s programs.
However, the distribution is not exactly the same across groups. For example, management-related master’s programs have a high share of beneficiaries: 1,221 beneficiaries compared with 208 selected applicants. This means that around 85% of students in this category became beneficiaries. Specialization programs also show a relatively high beneficiary share, while doctoral programs have a lower conversion rate, with 1,823 beneficiaries and 1,111 selected applicants.
This suggests that program type is related to the probability of becoming a beneficiary. Shorter or more professionally oriented programs, such as master’s degrees in administration or specializations, may be easier to finance and complete through Colfuturo compared with longer academic programs such as doctorates.
7.2 Chi-Square Test
Pearson's Chi-squared test
data: tabla_tipo
X-squared = 257.37, df = 4, p-value < 2.2e-16The chi-square test evaluates whether program type and applicant status are independent. The p-value is lower than 0.001, which indicates that there is a statistically significant association between program type and status. In other words, the distribution of beneficiaries and selected applicants differs across program types.
However, the warning message suggests that the chi-square approximation may be affected by categories with very small counts, such as Doctorado en Administración. Therefore, the result should be interpreted with caution for small categories. Even so, the general pattern is clear: program type is not randomly distributed between beneficiaries and selected applicants.
7.3 Top 10 Graduate University Analysis
Beneficiario Seleccionado
Not Top 10 14610 5745
Top 10 3434 1992
Pearson's Chi-squared test with Yates' continuity correction
data: tabla_elite
X-squared = 146.55, df = 1, p-value < 2.2e-16The cross-tabulation compares beneficiaries and selected applicants according to whether their graduate university belongs to the top 10 most frequent universities in the dataset. The results show that, outside the top 10 group, there are 14,610 beneficiaries and 5,745 selected applicants. In the top 10 group, there are 3,434 beneficiaries and 1,992 selected applicants.
This means that beneficiaries are more numerous in both groups, but the proportion of selected applicants is higher among students attending one of the top 10 most frequent graduate universities. In the “Not Top 10” group, selected applicants represent around 28% of the total, while in the “Top 10” group they represent around 37%. This suggests that selected applicants are relatively more concentrated in the most frequent graduate universities, while beneficiaries are more dispersed across a wider range of institutions.
The chi-square test confirms that this difference is statistically significant, since the p-value is lower than 0.001. Therefore, applicant status and graduate university group are not independent. However, this result should be interpreted as a difference in concentration, not as direct evidence of university quality, because the “Top 10” category is based on frequency in the dataset and not on an external academic ranking.
The proportional bar chart confirms the previous result visually. The share of selected applicants is higher among students in the top 10 most frequent graduate universities than among students in the rest of universities. In contrast, the share of beneficiaries is higher in the group of universities outside the top 10.
This suggests that the transition from selected applicant to beneficiary may vary depending on the type of university or the characteristics of the programs chosen. One possible explanation is that programs in the most frequent universities may be more expensive, more competitive, or located in countries with higher living costs, which could make it harder for some selected students to actually use the scholarship. Nevertheless, this interpretation should be taken as a possible explanation rather than a causal conclusion.
8 Part 6 - Further Study I
Yes, I am planning to pursue a postgraduate degree within the next five years. I would like to strengthen my academic and professional knowledge by specializing in areas such as finance or econometrics. I believe that pursuing a postgraduate degree would allow me to develop stronger analytical and quantitative skills, gain a deeper understanding of financial markets and economic analysis, and improve my ability to contribute to decision-making processes in both the public and private sectors.
9 Part 7 - Further Study II
If I had the opportunity to study abroad, I would like to study in New York or London because both cities are considered global financial and academic centers. They offer access to some of the best universities in the world, as well as strong connections with international financial institutions and companies.
In New York, universities such as Columbia University and New York University (NYU) are internationally recognized for their programs in finance, economics, and econometrics. These institutions are highly respected for their research quality and strong links with the financial industry.
In London, universities such as the London School of Economics (LSE), University College London (UCL), and Oxford University stand out for their excellence in economics, finance, and quantitative analysis. London is also one of the most important financial hubs in the world, which would provide valuable international exposure and professional opportunities.
10 Part 8 - Comparison with Colfuturo Beneficiaries
My answer is similar to the academic paths pursued by many Colfuturo beneficiaries, since a large number of them choose countries such as the United States and the United Kingdom to pursue postgraduate studies in highly ranked universities. Programs related to finance, economics, and econometrics are also common among Colfuturo scholars because of their strong academic reputation and their impact on professional development.
In terms of quality, the universities I mentioned, such as NYU, Columbia University, LSE, UCL, and Oxford, are internationally recognized and consistently appear among the top institutions in global rankings for economics, finance, and econometrics. Therefore, my interests and academic goals are aligned with the type of high-quality international education that many Colfuturo beneficiaries pursue.