This report summarizes the findings of the Consortium's third annual survey, which involved 25 colleges and more than 9,400 students. Participating colleges were responsible for sampling (based on a standardized procedure) and administering the survey in class. Completed questionnaires were then shipped to PRA Inc. for coding, data entry and analysis. The objectives of the research are to: provide national data on student access, time use and financing for Canadian college students from participating colleges; identify issues particular to certain learner groups or regions; and provide each institution with topline survey results (based on representative samples of their students), which may then be compared against the "national average". This dataset was freely received from the Canada Millennium Scholarship Foundation. Some work was required for the variable and value labels, and missing values. They were corrected as best as possible with the documentation received. Caution should be used with this dataset as some variables are lacking information.

The Canadian College Student Survey (the Consortium, CCSSC) includes the Association of Canadian Community Colleges (ACCC), individual participating colleges and the Canada Millennium Scholarship Foundation (CMSF). Established in late 2001, the Consortium conducted its first survey of college students in the spring of 2002. Some 27 colleges participated in this year's survey; each of them tried to have 300 to 450 of their students complete the survey depending on the size of institution. Individual colleges administered the survey using a sampling strategy and field guide provided by Prairie Research Associates (PRA) Inc. Approximately 9,900 students completed the survey. This dataset was freely received from the Canada Millennium Scholarship Foundation. Some work was required for the variable and value labels, and missing values. They were corrected as best as possible with the documentation received. Caution should be used with this dataset as some variables are lacking information.

The Canadian College Student Survey was conducted by the Canada Millennium Scholarship Foundation to provide data on student finances in Canada. The primary objective of the survey was to track the expenses and income of students on a monthly basis, in order to profile the financial circumstances of Canadian students and the adequacy of available funding. The survey will allow the Canada Millennium Scholarship Foundation to understand the financial circumstances of students who are in a post- secondary environment on an annual basis. This research is a joint effort of the Foundation, all participating colleges and the Association of Canadian Community Colleges (ACCC). The survey collects data on college students' income, expenditures and use of time. The survey is unique in that it provides national-level information on the challenges Canadian college students face in terms of financial and access issues. The objectives of the research are to: provide national-level data on s tudent access; time use and financing for Canadian college students from participating colleges; identify issues particular to certain learner groups and/or regions; and provide each institution with top-line survey results (based on representative samples of their students); which may then be compared against the "national average". In January 2003, the Foundation engaged Prairie Research Associates (PRA) Inc. to oversee this research. This dataset was freely received by the Canada Millennium Scholarship Foundation. Some work was required for the variable and value labels, and missing values. They were corrected as best as possible with the documentation received. Caution should be used with this dataset as some variables are lacking documentation.

Student Loan Market Outlook

According to our latest research, the global student loan market size reached USD 135.2 billion in 2024, reflecting the persistent demand for higher education financing worldwide. The market is expected to expand at a CAGR of 7.1% from 2025 to 2033, reaching an estimated USD 251.7 billion by 2033. This robust growth is driven by the increasing cost of tertiary education, rising enrollment rates, and evolving financial products tailored to diverse borrower needs. As per our latest analysis, the market is witnessing dynamic shifts in lender participation and repayment models, reflecting the changing landscape of global education finance.

One of the primary growth factors propelling the student loan market is the escalating cost of higher education across both developed and emerging economies. Tuition fees, living expenses, and ancillary costs have risen steadily, outpacing inflation and family income levels in many countries. This widening affordability gap has compelled students and their families to increasingly rely on external funding sources, particularly student loans. Simultaneously, the proliferation of private and alternative lenders has diversified borrowing options, making loans more accessible to a broader demographic. The emergence of income-driven repayment and refinancing solutions has further enhanced the market’s attractiveness, offering borrowers flexibility and financial relief over traditional rigid repayment structures.

Another significant factor impacting market growth is the ongoing digital transformation within the financial sector. Fintech innovations are streamlining loan origination, disbursement, and management, reducing operational costs for lenders and expediting the approval process for borrowers. Online lending platforms, powered by advanced analytics and AI, are enabling more personalized risk assessments and competitive interest rates, attracting tech-savvy students and parents. These platforms are also contributing to greater financial inclusion, particularly in regions where traditional banking infrastructure is limited. The integration of digital tools is not only enhancing the borrower experience but also improving portfolio performance for lenders through better risk management and customer engagement.

Demographic trends and government policies are also shaping the student loan market’s trajectory. The global surge in tertiary enrollment, especially in Asia Pacific and Africa, is expanding the borrower base. Governments in several countries are implementing supportive policies, such as interest subsidies, loan forgiveness programs, and flexible repayment schemes, to mitigate the financial burden on graduates and stimulate higher education participation. However, regulatory scrutiny around lending practices and concerns over rising student debt levels are prompting both public and private lenders to adopt more responsible lending and transparency measures. These dynamics are fostering a more balanced and sustainable growth environment for the student loan market.

Regionally, North America continues to command the largest share of the student loan market, driven by the United States’ mature lending ecosystem and high tertiary education costs. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid urbanization, expanding middle-class populations, and increasing investments in higher education infrastructure. Europe, meanwhile, exhibits steady growth, supported by government-backed loan schemes and cross-border education mobility. Latin America and the Middle East & Africa are witnessing gradual expansion, with rising demand for higher education and evolving financial services infrastructure. Each region presents unique challenges and opportunities, influencing lender strategies and market dynamics.

Type Analysis

The student loan market is segmented by type into federal loans, private loans, and refinancing loans, each with distinct characteristics and growth trajectories. Federal loans, primarily offered by government agencies, remain the dominant segment in markets such as the United States and several European countries. These loans typically feature lower interest rates, flexible repayment options, and borrower protections, making them the preferred choice for undergraduate and graduate students. The stability and accessibility of federal loans are underpinned by government backing, which reduces default ri

This statistic shows the average income of students in the Netherlands in 2017, by source (in euros). As of 2017, roughly *** euros of the students monthly average income came from their parents. Roughly *** euros, on the other hand, came from government loans.

The global student loans market is projected to reach a valuation of approximately USD 2.5 trillion by 2033, growing at a compound annual growth rate (CAGR) of 5.2% from 2025 to 2033.

The Canadian College Student Survey was conducted by the Canada Millennium Scholarship Foundation to provide data on student finances in Canada. The primary objective of the survey was to track the expenses and income of students on a monthly basis, in order to profile the financial circumstances of Canadian students and the adequacy of available funding. The survey will allow the Canada Millennium Scholarship Foundation to understand the financial circumstances of students who are in a post- secondary environment on an annual basis. This research is a joint effort of the Foundation, all participating colleges and the Association of Canadian Community Colleges (ACCC). The survey collects data on college students' income, expenditures and use of time. The survey is unique in that it provides national-level information on the challenges Canadian college students face in terms of financial and access issues. The objectives of the research are to: provide national-level data on student access; time use and financing for Canadian college students from participating colleges; identify issues particular to certain learner groups and/or regions; and provide each institution with top-line survey results (based on representative samples of their students); which may then be compared against the "national average".The Canada Millennium Scholarship Foundation commissioned R.A. Malatest and Associates Ltd. to conduct a comprehensive survey that provided national-level data concerning college students’ income, expenditures, levels of debt/perceptions of debt, and use of time. The 2002 Canadian College Student Survey Project was administered in March and April of 2002 in 16 colleges (representing 93,175 students). The maximum variation of the results of this survey is estimated to be ±1.2% (at a 95% confidence level). This dataset was freely received from the Canada Millennium Scholarship Foundation. Some work was required for the variable and value labels, and missing values. They were corrected as best as possible with the documentation received. Caution should be used with this dataset as some variables are lacking information.

The principal purposes of this national longitudinal study of the higher education system in the United States are to describe the characteristics of new college freshmen and to explore the effects of college on students. For each wave of this survey, each student completes a questionnaire during freshman orientation or registration that asks for information on academic skills and preparation, high school activities and experiences, educational and career plans, majors and careers, student values, and financing college. Other questions elicit demographic information, including sex, age, parental education and occupation, household income, race, religious preference, and state of birth. Specific questions asked of respondents in the 1982 survey pertained to PELL Grants and Guaranteed Student Loans (GSL), parents' status (full-time, part-time, lived stogether), whether students lived with their parents for more than two weeks of the year, whether students were listed as dependents on their parents' tax returns, and whether students received assistance worth $600 or more.

The average amount of non-mortgage debt held by consumers in the United States has been falling steadily during the past years, amounting to ****** U.S. dollars in 2023. While respondents had ****** U.S. dollars of debt in 2018, that volume decreased to ****** U.S. dollars in 2019, which constituted the largest year-over-year decrease.What age groups are more indebted in the U.S.?The age group with the highest level of consumer debt in the U.S. was belonging to the Generation X with approximately ******* U.S. dollars of debt in 2022. The next generations with high consumer debt levels were baby boomers and millennials, whose debt levels were similar. In comparison, credit card debt is more equally distributed across all ages. There is an exception among people under 35 years old, who are significantly less burdened with credit card debt. However, most consumers expect to get rid of their debt in the short term. College expenses as a source of debtEducational expenses were not among the leading sources of debt among consumers in the U.S. in 2022. Instead, they made up about ** percent of the total. However, around ** percent of undergraduates from lower-income families had student loans, while over a fifth of undergraduates from higher-income families had student loans. Independently of how they cover these expenses, the confidence of students and parents about being able to pay these college costs was high in most cases.

Pay‑for‑Performance Education Financing Market Outlook

According to our latest research, the global pay-for-performance education financing market size reached USD 5.2 billion in 2024, reflecting a robust demand for innovative funding mechanisms in the education sector. With a CAGR of 18.7% projected from 2025 to 2033, the market is expected to achieve a size of USD 28.3 billion by 2033. The primary growth driver is the increasing need for sustainable, outcome-driven education financing models that align stakeholders’ interests and promote accountability in educational outcomes.

One of the most significant growth factors propelling the pay-for-performance education financing market is the global shift towards skill-based and employability-focused education. Traditional tuition-based financing models are increasingly being scrutinized for their lack of alignment with actual student outcomes and job market demands. In contrast, pay-for-performance models such as income share agreements and outcome-based loans directly connect the cost of education to the success of graduates, incentivizing both institutions and students to prioritize career readiness. This alignment enhances the appeal of these models to students, parents, and employers alike, driving adoption across higher education, vocational training, and even K-12 segments. The growing prevalence of digital learning platforms and data analytics further enables precise tracking of outcomes, making these models more feasible and attractive to investors and policymakers.

Another key driver is the increasing involvement of private investors and social impact funds in education financing. As governments worldwide face budgetary constraints, there is a pressing need for alternative funding streams that can support educational access and quality without overburdening public finances. Pay-for-performance education financing, particularly through mechanisms like performance-based grants and outcome-based loans, offers a viable solution by tying funding to measurable results. This not only attracts private capital but also encourages educational institutions to innovate and improve their offerings. The rise of public-private partnerships and blended finance models is further accelerating the market’s growth, as stakeholders seek scalable and sustainable ways to address skills gaps and promote workforce development.

Technological advancements are also playing a pivotal role in the expansion of the pay-for-performance education financing market. The integration of advanced analytics, blockchain, and AI-powered platforms is enhancing transparency, streamlining contract management, and ensuring reliable outcome verification. These technologies reduce administrative overhead, minimize fraud, and increase stakeholder trust in the efficacy of pay-for-performance models. Furthermore, the proliferation of online learning and micro-credentialing is broadening the scope of education financing, enabling more personalized and flexible funding arrangements. As a result, the market is witnessing increased participation from non-traditional education providers, such as edtech startups and corporate training organizations, further diversifying the ecosystem and driving innovation.

From a regional perspective, North America continues to dominate the pay-for-performance education financing market, accounting for approximately 38% of global revenues in 2024. The region’s leadership is attributed to a well-established ecosystem of higher education institutions, robust regulatory frameworks, and significant venture capital investment in edtech and alternative financing. However, the Asia Pacific region is emerging as the fastest-growing market, with a projected CAGR of 22.4% through 2033, fueled by rising demand for vocational training, large youth populations, and government initiatives to improve educational outcomes. Europe, Latin America, and the Middle East & Africa are also witnessing increased adoption, driven by policy reforms and growing awareness of the benefits of outcome-based financing models.

Abstract

Private schools that rely entirely on student fees for financing are increasingly popular in many low-income countries and parents often prefer these schools to government-run ones. In Pakistan, children in these schools tend to outperform students in government-run schools. But financial constraints can limit the growth of these private schools, whose fees are set low to attract poor students, especially if they cannot access formal credit markets. Researchers from Pomona College, Harvard University and the World Bank have designed an impact evaluation to study private financing models - grants and loans - to support private schools in Pakistan.

The intervention centered on two financing approaches: a grant model and microloans. The program included a pilot microloan intervention to allow researchers to better develop and target loan products. This randomized control trial covered about 2,000 schools in about 650 villages across two districts in Punjab, Pakistan's most populous province.

Baseline, midline and endline surveys were conducted at both school and individual level. Within each survey, there were specific sections aimed to collect information from different perspectives. Thus, the survey initially included sections to be answered by the school owner, head teacher, class teacher, children, and operational head of the school. However, during the implementation process some changes were made in consultation with the World Bank's Strategic Impact Evaluation Fund (SIEF) and other parties involved. As a result, the final evaluation (or the endline survey) for this project shifted its focus to more specific objectives, concentrating on certain sections of initial surveys but also including additional components that would serve to the development of other projects.

The replication files for the associated American Economic Review (AER) Journal publication - Upping the Ante: The Equilibrium Effects of Unconditional Grants to Private Schools ("https://www.aeaweb.org/articles?id=10.1257/aer.20180924") are documented here for public use.

Geographic coverage

Rural areas of the district of Faisalabad in Punjab province.

Analysis unit

• schools
• individuals

Universe

The target population is low-cost private schools in rural areas of Faisalabad district. Sampling is at the village level, so urban, and peri-urban villages are excluded. Furthermore, for the design of the intervention, villages without any private schools, or with only one private school, are excluded. Villages with population over 10,000 or high village aggregated revenue are also excluded.

Government schools, and schools where money is not contained within the school itself (i.e. some network schools share money across multiple schools in the network), are also excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

All eligible schools that consented to participate across the 266 villages are included in the final randomization sample for the study. This includes 822 private and 33 NGO schools, for a total of 855 schools; there were 25 eligible schools (about 3 percent) that refused to participate in either the ballot or the surveys. The reasons for refusals were: impending school closure, lack of trust, survey burden, etc. Appendix Figure A1 of the Online Appendix (https://www.aeaweb.org/content/file?id=13118) summarizes the number of villages and schools in each experimental group.

Mode of data collection

Face-to-face [f2f]

Research instrument

Village Listing: This survey collects identifying data such as school names and contact numbers for all public and private schools in our sampling frame.

School Survey Long: This survey is administered twice, once at baseline in summer 2012 and again after treatment in the first follow-up round in May 2013. It contains two modules: the first module collects detailed information on school characteristics, operations and priorities; and the second module collects household and financial information from school owners. The preferred respondent for the first module is the operational head of the school, i.e. the individual managing day-to-day operations; if this individual was absent the day of the survey, either the school owner, the principal or the head teacher could complete the survey. For the second module, the preferred respondent was either the legal owner or the financial decision-maker of the school. In practice, the positions of operational head or school owner are often filled by the same individual.

School Survey Short: This survey is administered quarterly between October 2013 and December 2014, for a total of four rounds of data. Unlike the long school survey, this survey focuses on our key outcome variables: enrollment, fees, revenues and costs. The preferred respondent is the operational head of the school, followed by the school owner or the head teacher. Please consult Appendix Figure A3 of the Online Appendix (https://www.aeaweb.org/content/file?id=13118) to see the availability of outcomes across rounds.

Child Tests and Questionnaire: We test and collect data from children in our sample schools twice, once at baseline and once after treatment in follow-up round 3. Tests in Urdu, English and Mathematics are administered in both rounds; these tests were previously used and validated for the LEAPS project (Andrabi et al., 2002). Baseline child tests are only administered to a randomly selected half of the sample (426 schools) in November 2012. Testing is completed in 408 schools for over 5000 children, primarily in grade 4. If a school had zero enrollment in grade 4 however, then the preference ordering of grades to test was grade 3, 5, and then 6. A follow-up round of testing was conducted for the full sample in January 2014. We tested two grades between 3 and 6 at each school to ensure that zero enrollment in any one grade still provided us with some test scores from every school. From a roster of 20,201 enrolled children in this round, we tested 18,376 children (the rest were absent). For children tested at baseline, we test them again in whichever grade they are in as long as they were enrolled at the same school. We also test any new children that join the baseline test cohort. In the follow-up round, children also complete a short survey, which collects family and household information (assets, parental education, etc.), information on study habits, and self-reports on school enrollment.

Teacher Rosters: This survey collects teacher roster information from all teachers at a school. Data include variables such as teacher qualifications, salary, residence, tenure at school and in the profession. It was administered thrice during the project period, bundled with other surveys. The first collection was combined with baseline child testing in November 2012, and hence data was collected from only half of the sample. Two follow-up rounds with the full sample took place in May 2013 (round 1) and November 2014 (round 5).

Investment Plans: These data are collected once from the treatment schools as part of the disbursement activities during September-December 2012. The plans required school owners to write down their planned investments and the expected increase in revenues from these investments— whether through increases in enrollment or fees. School owners also submitted a desired disbursement schedule for the funds based on the timing of their investments.

Study Abroad Market Outlook

According to our latest research, the global study abroad market size reached USD 129.5 billion in 2024, demonstrating robust expansion driven by increased international student mobility and rising demand for global educational experiences. With a compound annual growth rate (CAGR) of 9.2% expected from 2025 to 2033, the market is forecasted to attain a substantial value of USD 282.7 billion by 2033. This growth is fueled by the proliferation of cross-border academic collaborations, the rising appeal of multicultural learning environments, and a strong emphasis on employability through international exposure.

The growth of the study abroad market is primarily attributed to the intensifying globalization of higher education. Universities worldwide are forging partnerships, launching dual degree programs, and offering transfer pathways to attract international students. The value proposition of studying abroad has evolved beyond academic excellence to include immersive cultural experiences, language acquisition, and the development of global competencies. These factors are increasingly recognized by employers, prompting both students and their families to invest in international education as a strategic career move. Moreover, governments in key destination countries are implementing favorable visa policies and post-study work opportunities, further incentivizing students to pursue education overseas.

Technological advancements have also played a pivotal role in expanding the reach of the study abroad market. The rise of digital platforms and virtual counseling services has democratized access to information, allowing prospective students from diverse backgrounds to explore and apply to programs across the globe. EdTech innovations, such as AI-powered application guidance and virtual campus tours, have streamlined the decision-making process, making it easier for students to evaluate options and navigate complex application procedures. Additionally, the proliferation of online preparatory courses and language training programs has enabled students to better prepare for academic and cultural transitions, reducing barriers to entry and increasing the volume of outbound students.

Socioeconomic factors, including the growing middle class in emerging economies and increased disposable income, have significantly contributed to the expansion of the study abroad market. Parents are increasingly willing to invest in their children's global education, perceiving it as a pathway to upward mobility and enhanced career prospects. Furthermore, scholarship programs, financial aid, and student loan products have become more accessible, enabling a broader demographic to participate in international education. The market also benefits from the efforts of destination countries to diversify their international student base, actively recruiting talent from underrepresented regions through targeted marketing campaigns and partnerships with local educational institutions.

Regionally, Europe and North America continue to dominate as preferred destinations, collectively accounting for more than 60% of the global study abroad market in 2024. However, Asia Pacific is emerging as both a significant source and destination region, driven by the rapid expansion of its higher education sector and increasing outbound mobility from countries like China and India. Latin America and the Middle East & Africa are also witnessing growing interest, supported by government initiatives to internationalize their educational offerings and attract foreign students. This regional diversification is expected to intensify competition among destination countries, leading to innovative program offerings and enhanced student support services.

Program Type Analysis

The study abroad market is segmented by program type into undergraduate, postgraduate, short-term, language programs, exchange programs, and others. Undergraduate programs represent the largest share of the market, with a substantial proportion of international students opting for full-degree programs at the bachelor’s level. The appeal of undergraduate study abroad lies in the opportunity for early immersion in a new academic system, exposure to diverse perspectives, and access to world-class faculty and resources. Institutions in North America and Europe, in particular, have seen a steady influx of undergraduate international students, driv

Costa Rica Foreign Direct Investment Income: Outward: Total: Education data was reported at 0.000 USD mn in 2023. This stayed constant from the previous number of 0.000 USD mn for 2022. Costa Rica Foreign Direct Investment Income: Outward: Total: Education data is updated yearly, averaging 0.000 USD mn from Dec 2017 (Median) to 2023, with 7 observations. The data reached an all-time high of 0.000 USD mn in 2023 and a record low of 0.000 USD mn in 2023. Costa Rica Foreign Direct Investment Income: Outward: Total: Education data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Costa Rica – Table CR.OECD.FDI: Foreign Direct Investment Income: by Industry: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positionsTreatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). Resident Special Purpose Entities (SPEs) do not exist or are not significant and are recorded as zero in the FDI database. Valuation method used for listed inward and outward equity positions: Book value. Valuation method used for unlisted inward and outward equity positions: Book value. Valuation method used for inward and outward debt positions: Book value. .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. Direct investment relationships are identified according to a partial application of the criteria of the Framework for Direct Investment Relationships (FDIR) method. Debt between fellow enterprises are covered. Non-profit institutions serving households are not covered as direct investors. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward and outward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise/direct investor. Outward FDI transactions and positions are allocated according to the activity of the resident direct investor. Statistical unit: Enterprise.

This is a special microdata file containing selected asset, debt and housing information for economic family units as collected in the 1984 Survey of Consumer Finances. Earlier surveys on family assets and debts were conducted in 1956, 1959, 1964, 1970 and 1977. The results of these surveys are published in Catalogue Numbers 13-508, 13-514, 13-525, 13-547 and 13-572 respectively, and differences in concepts and survey coverage are available by consulting these reports. The reference year for this file is 1983. Commencing with the 1998 microdata files, annual cross-sectional income data will be sourced from the Survey of Labour and Income Dynamics (SLID).

Lithuania LT: Foreign Direct Investment Income: Outward: Total: Education data was reported at 0.800 EUR mn in 2023. This records an increase from the previous number of -0.040 EUR mn for 2022. Lithuania LT: Foreign Direct Investment Income: Outward: Total: Education data is updated yearly, averaging 0.000 EUR mn from Dec 2005 (Median) to 2023, with 16 observations. The data reached an all-time high of 1.420 EUR mn in 2017 and a record low of -0.040 EUR mn in 2022. Lithuania LT: Foreign Direct Investment Income: Outward: Total: Education data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.FDI: Foreign Direct Investment Income: by Industry: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market and Nominal values. .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

Luxembourg LU: Foreign Direct Investment Income: Inward: Total: Education data was reported at 0.000 EUR mn in 2023. This stayed constant from the previous number of 0.000 EUR mn for 2022. Luxembourg LU: Foreign Direct Investment Income: Inward: Total: Education data is updated yearly, averaging 0.000 EUR mn from Dec 2012 (Median) to 2023, with 11 observations. The data reached an all-time high of 0.000 EUR mn in 2023 and a record low of 0.000 EUR mn in 2023. Luxembourg LU: Foreign Direct Investment Income: Inward: Total: Education data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Luxembourg – Table LU.OECD.FDI: Foreign Direct Investment Income: by Industry: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market value, Nominal value.; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. Direct investment relationships are identified according to the criteria of the Framework for Direct Investment Relationships (FDIR) method. Debt between fellow enterprises are completely covered. Collective investment institutions are not covered as direct investment enterprises. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions and positions are allocated according to the activity of the resident direct investor. Statistical unit: Enterprise.