The UK student loan market, a significant segment of the global student loan landscape, is experiencing robust growth fueled by increasing higher education enrollment and evolving government policies. While precise market figures for the UK specifically are unavailable from the provided data, we can infer substantial size based on the global CAGR of 7% and the presence of major UK lenders like HSBC and others listed. The market is segmented by loan type (federal/government, private), repayment plan (standard, graduated, income-based, etc.), age group (under 24, 25-34, over 35), and end-user (graduate, high school, other). Government loan programs, due to their accessibility and affordability, likely dominate the market share. However, the private student loan segment is also witnessing growth, driven by demand for specialized financing and potentially higher borrowing limits than government schemes. Trends like rising tuition fees and the increasing awareness of income-driven repayment plans contribute to market expansion. Conversely, constraints include potential economic downturns that could impact borrower repayment ability and government policy shifts affecting loan availability or terms. The market's future growth will depend on factors such as government funding levels for higher education, economic conditions, and the continued popularity of higher education among young people. Further analysis suggests that the market's regional concentration is largely within the UK, though international students studying in the UK contribute to the overall value. Competition among lenders is intense, encompassing both large established banks and specialized student loan providers. The competitive landscape necessitates innovative product offerings, competitive interest rates, and flexible repayment options to attract and retain borrowers. The sustained growth trajectory indicates a promising outlook for the UK student loan market, with opportunities for further expansion driven by ongoing trends in education and economic factors. Data points to considerable growth potential across all segments. However, careful monitoring of economic indicators and regulatory changes will be crucial for stakeholders to effectively navigate the market's future landscape. Recent developments include: July 2023: Prodigy Finance, a socially responsible FinTech leader in international student loan lending, announced a groundbreaking USD 350 million facility in partnership with Citi, Schroders Capital, and SCIO Capital. This marks the inaugural transaction under Prodigy's innovative multi-issuance special-purpose vehicle structure. The collaborative effort between Prodigy Finance and its funding partners reflects a substantial commitment to providing accessible financial support to ambitious master's students worldwide. To date, Prodigy has disbursed over USD 1.8 billion in postgraduate education loans, supporting more than 35,000 high-potential students from across 100 different countries., March 2023: Following extensive overnight negotiations, HSBC came to the rescue of Silicon Valley Bank's UK branch. HSBC UK has acquired SVB UK for a nominal sum of GBP 1 (USD 1.21) in a transaction that excludes the assets and liabilities of SVB UK's parent company.. Key drivers for this market are: Increasing Demand for Higher Education is Driving the Market, Government Support is Driving the Market. Potential restraints include: Increasing Demand for Higher Education is Driving the Market, Government Support is Driving the Market. Notable trends are: High Tuition Fees is Driving the Market.

Financial overview and grant giving statistics of Student Partner Alliance A New Jersey Non-Profit Corporation

Net-Income-Including-Non-Controlling-Interests Time Series for SoFi Technologies Inc.. SoFi Technologies, Inc. provides various financial services in the United States, Latin America, Canada, and Hong Kong. It operates through three segments: Lending, Technology Platform, and Financial Services. The company offers lending and financial services and products that allows its members to borrow, save, spend, invest, and protect money; and personal loans, student loans, home loans, and related services. The company also operates Galileo, a technology platform that offers services to financial and non-financial institution; and Technisys, a cloud-native digital and core banking platform that provides software licenses and associated services, including implementation and maintenance. In addition, it provides SoFi Money offers checking and savings accounts, and cash management products; and SoFi Invest, a mobile-first investment platform that offers access to trading and advisory solutions, such as investing and robo-advisory. Further, the company offers SoFi Credit Card that provides cash back rewards on every purchase; Sofi Relay, a personal finance management product that allows to track all of their financial accounts comprising credit score and spending behaviors; SoFi Protect which offers insurance product; SoFi Travel, an application that manages travel search and booking experience; SoFi At Work provides financial benefits to employees, including student loan payments made on their employees' behalf; Lantern Credit, a financial services marketplace platform for seeking alternative products and provide product comparisons; and other lending as a service that offers pre-qualified borrower referrals and offers loans to third-party partner. The company was founded in 2011 and is based in San Francisco, California.

See notice below about this dataset

This dataset provides the average earnings by student group per district.  Wage records are obtained from the Massachusetts Department of Unemployment Assistance (DUA) using a secure, anonymized matching process with limitations. For details on the process and suppression rules, please visit the Employment and Earnings of High School Graduates dashboard.

This dataset is one of three containing the same data that is also published in the Employment and Earnings of High School Graduates dashboard: Average Earnings by Student Group Average Earnings by Industry College and Career Outcomes

2025 Update on DESE Data on Employment and Earnings 

The data link between high school graduates and future earnings makes it possible to follow students beyond high school and college into the workforce, enabling long-term evaluation of educational programs using workforce outcomes.

While DESE has published these data in the past, as of June 2025 we are temporarily pausing updates due to an issue conducting the link that was brought to our attention in 2023 by a team of researchers. The issue impacts the earnings information for students who never attended a postsecondary institution or who only attended private or out-of-state colleges or universities, beginning with the 2017 high school graduation cohort, with growing impact in each successive high school graduation cohort.

The issue does not impact the earnings information for students who attended a Massachusetts public institution of higher education, and earnings data for those students will continue to be updated.

Once a solution is found, the past cohorts of data with low match rates will be updated. DESE and partner agencies are exploring linking strategies to maximize the utility of the information.

More detailed information can be found in the attached memo provided by the research team from the Annenberg Institute. We thank them for calling this issue to our attention.

Net-Interest-Income Time Series for Fiserv, Inc.. Fiserv, Inc. provides payments and financial services technology solutions in the United States, Europe, the Middle East and Africa, Latin America, the Asia-Pacific, and internationally. It operates through the Merchant Solutions and Financial Solutions segments. The company provides merchant acquiring and digital commerce services, mobile payment services, security and fraud protection solutions, stored-value solutions, software-as-a-service, and pay-by-bank solutions, as well as Clover, a point-of-sale and business management platform through various channels, including direct sales teams, strategic partnerships with agent sales forces, independent software vendors, independent sales organizations, financial institutions, and other strategic partners. It also offers debit card processing services, debit network services, security and fraud protection products, bill payment; person-to-person payments, account-to-account transfers, credit card processing services, prepaid card processing services, card production services, print services, government payment processing, student loan processing, and customer loan and deposit account processing; digital banking; financial and risk management; and professional services and consulting, check processing, automated clearing house, and real-time payments. It serves large enterprise, small business, banks, credit union, large financial institution, fintech, public sectors, and software providers. Fiserv, Inc. was incorporated in 1984 and is headquartered in Milwaukee, Wisconsin.

Many of your staff, grant recipients and partners may be eligible for loan forgiveness. Typically, to quality you must be employed by a U.S. federal, state, local, or tribal government, a 501(c)3 non-profit or a non-profit organization that provides a qualifying service (including military service).

You can tailor these resources to spread the word about the PSLF program. Please consider sharing in your newsletters, social media feeds or at grant recipient convenings and conferences!

Subject: Changes to Public Service Loan Forgiveness (PSLF) Program Offer More Options for Loan Forgiveness

[INSERT STATE] Employees May Now Be Eligible

The COVID-19 pandemic resulted in financial hardship for many, including members of the human services workforce. As a [INSERT STATE] employee, you may now be eligible for federal student loan forgiveness for your important public service, even if you were not eligible before.

ACF has created a PSLF landing page that includes resources for you to share. It includes the March 31 webinar

hosted by the Office of Early Childhood Development, in partnership with the Department of Education, attended by over 17,000 early educators. A webinar for the broader human services community was held on May 26th. Both recordings, as well as PDFs and Frequently Asked Questions, are housed on the site. Please help us share this news with the broader human services workforce, including all of you who work here at [INSERT STATE].

The Department of Education issued a waiver that allows you to get credit for past payments even if you didn’t make the payment on time, didn’t pay the full amount due, or weren’t on a the right repayment plan. Until Oct. 31, 2022, federal student loan borrowers can get credit for payments that previously didn’t qualify for Public Service Loan Forgiveness (PSLF). Many people in the human services sector (including those that work in government and nonprofits) qualify for this program but don’t know about it. See if you qualify

.

Because of the COVID-19 emergency, the U.S. Department of Education announced a change to Public Service Loan Forgiveness (PSLF) program rules. For a limited time, borrowers may receive credit for past periods of repayment that would otherwise not qualify for loan forgiveness. The waiver expires October 31, 2022. See if you qualify and apply today

!

Did you know that for a limited time, borrowers may receive credit for past periods of repayment that would otherwise not qualify for the Public Service Loan Forgiveness program? Read the FAQs to learn more and see if you qualify.

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Metadata-only record linking to the original dataset. Open original dataset below.

Financial burdens of the parental home through education of children.

Topics: Start of studies; length of studies; amount of money available to the student monthly; current income and burden conditions of parents; opportunities to finance studies; stay of student in semester breaks; attitude of student to work in semester breaks; readiness of parents to finance studies; degree of familiarity of the Honnef Model; detailed information on income and contributions of the student as well as the remaining children; housing situation and rent costs of respondent.

Demography: income; household income; size of household; social origins; city size; state; refugee status; possession of durable economic goods; possession of assets.

Price-Earnings-Ratio Time Series for SoFi Technologies Inc.. SoFi Technologies, Inc. provides various financial services in the United States, Latin America, Canada, and Hong Kong. It operates through three segments: Lending, Technology Platform, and Financial Services. The company offers lending and financial services and products that allows its members to borrow, save, spend, invest, and protect money; and personal loans, student loans, home loans, and related services. The company also operates Galileo, a technology platform that offers services to financial and non-financial institution; and Technisys, a cloud-native digital and core banking platform that provides software licenses and associated services, including implementation and maintenance. In addition, it provides SoFi Money offers checking and savings accounts, and cash management products; and SoFi Invest, a mobile-first investment platform that offers access to trading and advisory solutions, such as investing and robo-advisory. Further, the company offers SoFi Credit Card that provides cash back rewards on every purchase; Sofi Relay, a personal finance management product that allows to track all of their financial accounts comprising credit score and spending behaviors; SoFi Protect which offers insurance product; SoFi Travel, an application that manages travel search and booking experience; SoFi At Work provides financial benefits to employees, including student loan payments made on their employees' behalf; Lantern Credit, a financial services marketplace platform for seeking alternative products and provide product comparisons; and other lending as a service that offers pre-qualified borrower referrals and offers loans to third-party partner. The company was founded in 2011 and is based in San Francisco, California.

Finanzielle Belastungen des Elternhauses durch die Ausbildung derKinder. Themen: Studienbeginn; Studiendauer; Höhe des dem Studenten monatlichzur Verfügung stehenden Geldes; gegenwärtige Einkommens- undBelastungsverhältnisse der Eltern; Finanzierungsmöglichkeiten desStudiums; Aufenthalt des Studenten in den Semesterferien; Einstellungzur Arbeit des Studenten in den Semesterferien; Bereitschaft der Elternzur Studienfinanzierung; Bekanntheitsgrad des Honnefer Modells;detaillierte Angaben über die Einkünfte und Zuwendungen des Studentensowie der übrigen Kinder; Wohnsituation und Mietkosten des Befragten. Demographie: Einkommen; Haushaltseinkommen; Haushaltsgröße; sozialeHerkunft; Ortsgröße; Bundesland; Flüchtlingsstatus; Besitz langlebigerWirtschaftsgüter; Besitz von Vermögen. Financial burdens of the parental home through education of children. Topics: Start of studies; length of studies; amount of money availableto the student monthly; current income and burden conditions ofparents; opportunities to finance studies; stay of student in semesterbreaks; attitude of student to work in semester breaks; readiness ofparents to finance studies; degree of familiarity of the Honnef Model;detailed information on income and contributions of the student as wellas the remaining children; housing situation and rent costs ofrespondent. Demography: income; household income; size of household; socialorigins; city size; state; refugee status; possession of durableeconomic goods; possession of assets. Mündliche Befragung mit standardisiertem Fragebogen Oral survey with standardized questionnaire Eltern von Studenten, die im WS 1960/61 immatrikuliert waren

See notice below about this dataset

This dataset provides the number of graduates who enrolled in each type of postsecondary education per district.

Wage records are obtained from the Massachusetts Department of Unemployment Assistance (DUA) using a secure, anonymized matching process with limitations. For details on the process and suppression rules, please visit the Employment and Earnings of High School Graduates dashboard.

This dataset is one of three containing the same data that is also published in the Employment and Earnings of High School Graduates dashboard: Average Earnings by Student Group Average Earnings by Industry College and Career Outcomes

List of Outcomes

• Total Postsecondary Enrollment
• In-State Public 2-Year
• In-State Public 4-Year
• In-State Private
• Out-of-State
• Total Employed
• Total Missing

The data link between high school graduates and future earnings makes it possible to follow students beyond high school and college into the workforce, enabling long-term evaluation of educational programs using workforce outcomes.

While DESE has published these data in the past, as of June 2025 we are temporarily pausing updates due to an issue conducting the link that was brought to our attention in 2023 by a team of researchers. The issue impacts the earnings information for students who never attended a postsecondary institution or who only attended private or out-of-state colleges or universities, beginning with the 2017 high school graduation cohort, with growing impact in each successive high school graduation cohort.

The issue does not impact the earnings information for students who attended a Massachusetts public institution of higher education, and earnings data for those students will continue to be updated.

Once a solution is found, the past cohorts of data with low match rates will be updated. DESE and partner agencies are exploring linking strategies to maximize the utility of the information.

More detailed information can be found in the attached memo provided by the research team from the Annenberg Institute. We thank them for calling this issue to our attention.

For the incoming class of 2021, the average MBA applicant planned to pay for business school through a mix of financing sources. 26 percent of the total expenses needed for their business school experience was allocated to grants, fellowships, and scholarships while 25 percent was allocated to loans. Personal savings, parental support, personal earnings, and employer reimbursement made up the rest.

The Survey of Consumer Finances (SCF) is normally a triennial cross-sectional survey of U.S. families. The survey data include information on families' balance sheets, pensions, income, and demographic characteristics.

This database contains the survey sample (FinalDataBase.dta), the command lines (FinalAnalysis.do) and the survey questionnaire in Portuguese (FinalForm.pdf) for replicating the article results. The analyzes were carried out in the Stata 14 software and commented in Portuguese.

Article abstract:

The recent democratization of credit has allowed the population to increase its consumption pattern, however, the level of financial knowledge has not kept up with this growth, which is a trigger for serious financial problems. The present study aimed to measure the level of financial knowledge in university students verifying if there is a difference between incoming students and graduates and in addition to analyze if there is any difference in the domain of the theme between several undergraduate courses such as engineering, business and humanities (i.e. among students of courses with different levels of mathematical education). For this a questionnaire was applied and refined using the Item Response Theory (IRT), validated by the Differential Item Functioning (DIF) test. The sample had 232 students and the results concluded that there is a difference in financial knowledge between the groups, being higher in students of the last year and in students of engineering and business courses, to those who have a less dense mathematical education, which may indicate that mathematics helps for better financial literacy of the individual. Additionally, differences in financial knowledge by gender, races/ethnicities, income and parental education were also found.

Resumo do artigo:

A recente democratização do crédito tem permitido a população um aumento em seu padrão de consumo, no entanto, o nível de conhecimento financeiro não acompanhou tal crescimento, sendo isso um gatilho para sérios problemas financeiros. O presente estudo teve como objetivo medir o nível de conhecimento financeiro em estudantes universitários, verificando se existe diferença entre calouros e egressos, e, além disso, analisar se existe alguma diferença no domínio do tema entre diversos cursos de graduação como engenharias, de negócios e de humanas (i.e entre alunos com níveis diferentes de educação matemática). Para isso, foi aplicado um questionário que foi refinado utilizando a Teoria de Resposta ao Item (TRI), validada pelo teste DIF (Differential Item Functioning). A amostra contou com 232 estudantes e os resultados concluíram que existe diferença no conhecimento financeiro entre os grupos, sendo maior em estudantes do último ano e em alunos dos cursos de engenharias e de negócios e entre àqueles que possuem uma educação matemática mais sólida, o que pode indicar que a matemática ajuda para uma melhor alfabetização financeira do indivíduo. Adicionalmente, também foram encontradas diferenças de conhecimento financeiro por gênero, raças/etnias, renda e escolaridade dos pais.

Student Performance Prediction in Higher Education Dataset Description This dataset contains data representing student performance in higher education institutions across Australia. The dataset is designed to aid in the prediction of student performance based on a variety of academic, personal, and socio-economic factors. some data including university names have been removed for privacy concerns.

Dataset Summary Total Records: 100,256 Total Features: 51 Target Variable: Performance Features Student ID: Unique identifier for each student. University ID: Unique identifier for each university. University Name: Name of the university. Age: Age of the student. Gender: Gender of the student. Major: Student's major or field of study. Year of Study: Current year/level of study (e.g., freshman, sophomore). GPA: Grade Point Average. High School GPA: GPA from high school. Entrance Exam Score: Score on university entrance exams. Attendance Rate: Percentage of classes attended. Participation in Extracurricular Activities: Whether the student participates in extracurricular activities (0 = No, 1 = Yes). Part-time Job: Whether the student has a part-time job (0 = No, 1 = Yes). Hours of Study per Week: Average number of hours spent studying per week. Family Income: Family's annual income. Parental Education Level: Highest education level attained by parents. Accommodation Type: Type of accommodation (Dormitory, Off-campus, With family). Distance from Home to University: Distance between student's home and the university. Internet Access at Home: Whether the student has internet access at home (0 = No, 1 = Yes). Library Usage: Frequency of library usage (number of visits per week). Access to Academic Resources: Availability of academic resources (0 = No, 1 = Yes). Health Condition: Student's health condition (Excellent, Good, Fair, Poor). Mental Health Status: Self-reported mental health status (Excellent, Good, Fair, Poor). Scholarship: Whether the student receives a scholarship (0 = No, 1 = Yes). Financial Aid: Whether the student receives financial aid (0 = No, 1 = Yes). Tutor Support: Whether the student has access to a tutor (0 = No, 1 = Yes). Counseling Services: Whether the student uses counseling services (0 = No, 1 = Yes). Transportation Mode: Mode of transportation to university (Walking, Biking, Public Transport, Car). Hours of Sleep per Night: Average number of hours slept per night. Diet Quality: Self-reported diet quality (Excellent, Good, Fair, Poor). Exercise Frequency: Frequency of exercise per week. Social Integration: Level of social integration within the university (Excellent, Good, Fair, Poor). Peer Support: Availability of peer support (0 = No, 1 = Yes). Language Proficiency: Proficiency in the language of instruction (Excellent, Good, Fair, Poor). Use of Online Learning Platforms: Frequency of using online learning platforms. Class Participation: Level of participation in class discussions (Excellent, Good, Fair, Poor). Project/Assignment Scores: Average scores on projects and assignments. Midterm Exam Scores: Scores on midterm exams. Final Exam Scores: Scores on final exams. Attendance at Office Hours: Frequency of attending professors' office hours. Group Work Participation: Participation in group work (0 = No, 1 = Yes). Research Involvement: Involvement in research projects (0 = No, 1 = Yes). Internship Experience: Whether the student has internship experience (0 = No, 1 = Yes). Peer Reviews: Scores or feedback from peer reviews. Academic Advising: Frequency of meetings with academic advisors. Learning Style: Preferred learning style (Visual, Auditory, Kinesthetic, Reading/Writing). Study Environment: Quality of study environment (Excellent, Good, Fair, Poor). Core Course Average: Average scores in core courses. Extracurricular Participation: Level of participation in extracurricular activities (0 = No, 1 = Yes). Peer Evaluations: Peer feedback on collaborative work. Performance: Overall performance label (Excellent, Good, Satisfactory, Needs Improvement, Poor). Target Variable - Performance The target variable Performance is a categorical feature representing the overall performance of the student. The possible values are:

Excellent: Top-performing students. Good: Above-average performance. Satisfactory: Average performance. Needs Improvement: Below-average performance. Poor: Poor performance.

Usage

This dataset can be used for:

Predictive modeling to identify factors influencing student performance. Analyzing trends and patterns in student performance across different universities. Developing interventions to support students at risk of poor performance. Acknowledgements This dataset provides a rich resource for researchers and educators interested in student performance prediction and the factors that influence academic success in higher education institutions in Australia.

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 1980 survey included information regarding the BEOG (Basic Educational Opportunity Grant) and GSL (Guaranteed Student Loan) financial aid programs, students' number of brothers and sisters, whether students considered themselves born-again Christians, and whether students considered themselves physically handicapped.

This survey was designed to address such topics as: - access to postsecondary education; - part-time and full-time study and employment; - students' income and expenditures; - issues such as mobility, language and Canadian Studies. Major variable categories include: Geographic variables, Choosing an institution, Choosing a field of study, Choosing level of education, Current registration details, Choosing part-time versus full-time, Co-op program, Courses on Canada, Language courses, Language of instruction, Education prior to current registration, Major activity prior to registering, Labour force activity in reference week, Expected activity post-graduation, Demographic variables, Socio-economic variables, Financing education, Income by source, R and spouse, Financial expenditures, Sample weight

Metric scores are not reported for n-sizes under 10. Per OSSE's policy, secondary suppression is applied to all student groups when a complementary group has an n-size under 10 or is top/bottom suppressed to prevent the calculation of suppressed data.

Data Source: DC Office of the State Superintendent of Education

Why This Matters

A growing number of jobs require college degrees and people with college degrees tend to have higher incomes.

Although bachelor’s degree attainment has increased across all racial and ethnic groups, inequities persist and factors such as family income, parental education level, and neighborhood segregation continue to act as barriers to college enrollment.

Racial disparities in educational attainment perpetuate other racial inequities including in employment opportunities, wages earned, occupations held, and overall well-being.

The District Response

The Office of the State Superintendent of Education (OSSE)’s Division of Postsecondary and Career Education (PCE) helps residents transition into postsecondary programs. They offer career guidance, help students find and apply to grants, and assist residents in obtaining adult literacy proficiency and GED credentials.

The Office of College and Career Readiness (CCR) promotes college access for public school students by offering academically rigorous programs, providing funding for SAT and ACT college entrance exams, and promoting FAFSA and college application completion.

The Tuition Assistance Program Initiative for TANF (TAPIT) provides financial assistance for TANF customers to pursue postsecondary degrees of college certificate programs. This can lower the financial barrier low-income residents face in pursuing higher education.

Public schools have managed to maintain revenue growth despite significant shifts in funding, enrollment and parental preferences. Class sizes are shrinking every year as birth rates drop and the high school retention rate stagnates, straining revenue as smaller schools see lessened funding from governments. Public schools have contended with heightened competition from alternative education options, especially homeschooling and private institutions, as parents seek more personalized educational experiences. States have increasingly adopted school choice systems, allowing parents to use public funds or tax credits to pay for private schooling. The Trump administration has taken steps to promote these programs even more and has proposed establishing a federal voucher system. Despite heightened competition and a rigorous competitive atmosphere, strong per-pupil funding amid strong state and local budgets has buoyed public schools. Public schools' revenue has been climbing at a CAGR of 1.4% to an estimated $1.0 billion over the five years through 2025, including a rise of 0.9% in 2025 alone. Governments fully fund public schools. Support from state and local governments is especially vital, as they provide nearly nine-tenths of public schools' revenue. Despite a slight dip in 2022, strong tax income pushed up government funding for primary and secondary schools by 6.2% in 2023. These resources are enabling public schools to invest in tutoring and counseling to improve their educational outcomes and better compete with alternative primary and secondary schools. Public schools also used funds to help transition to online and augmented education and have avoided taking on further losses as shrinking class sizes leave them without pressure to continue purchasing new laptops or tablets. Still, public schools are not profitable and largely operate at a loss every year. Public schools are set to face a continued drop in enrollment as well as intensifying competition. To sustain revenue and support, schools will focus on retaining students and improving academic outcomes despite potential federal funding changes. The expansion of school choice programs will compel public schools to enhance their quality and offer additional services like after-school programs to sustain enrollment and win parental support as families gain more access to private schools. Still, charter schools will leverage their unique value propositions to remain competitive and buoy enrollment in the public school system. Public schools' revenue is set to stagnate, swelling at a CAGR of just 0.2% to an estimated $1.0 billion through the end of 2030.

The student survey data include measures of students’ background, knowledge, aspirations, satisfaction, and perception of classroom environment and teaching quality. The student survey measures are drawn from previous national and international studies including TRIPOD, Evaluating the Design and Impact of School Sector Development Program (SSDP) Training in Nepal (3ie) and School Sector Reform Program (World Bank). Out of 4889 eligible students, data were collected from 4588 students resulting in a response rate of 94%.

The parent survey data include household-level measures of household size, composition, socio-economic background (ethnicity, social status), education and occupation, wealth, assets, and income. The data also include individual-level measures of parents’ perceptions of teaching quality, parental action related to gathering information about alternative schools, barriers/facilitators to exercising school choice, and awareness and participation in civil society organizations seeking to influence governance of education. Out of 4889 eligible students’ parents, data were collected from 4606 parents resulting in a response rate of 94%.

The teacher survey data include measures of teachers’ background, knowledge, training, instructional practice, classroom management, and parental outreach and reporting. Out of 951 eligible teachers, data were collected from 906 teachers resulting in a response rate of 95%.

The Head Teacher (Principal) survey data include measures of general background, performance, school management experience, professional engagement with school leadership, and satisfaction with working conditions. Out of 114 eligible schools, data were collected from Head Teachers (Principals) from 113 schools resulting in a response rate of 99%.

The school management committee (SMC) data include measures of accountability (delegation, finance, performance, information, and enforcement). Out of 114 eligible schools, data were collected from SMCs from 109 schools resulting in a response rate of 96%.

The Parent Teacher Association (PTA) data include measures of short-term accountability (local community level) and other measures of accountability (delegation, finance, performance, information, and enforcement). Out of 114 eligible schools, data were collected from PTA representatives from 108 schools resulting in a response rate of 95%.

We propose to develop and validate measures of accountability to be shared with the Nepal Ministry of Education (MOE), local stakeholders and scientific communities, and to use those measures in an analysis of the determinants of accountability and its association with students' gains in achievement. The proposed study will build on the resources of the Chitwan Valley Family Study (CVFS), a 20-year ongoing panel study of 116 schools with 3,000 households with 3,500 school aged children in 151 communities located throughout the Western Chitwan Valley of Nepal. With funding from DFID-ESRC, we are proposing to achieve two aims: Aim One: To Develop and Pretest a Suite of Nepali Accountability Assessment Tools (NAATs) for Use by the MOE and to Pilot these Tools within the Chitwan Valley of Nepal. Importantly, the tools will be designed so that Nepal's MOE can both assess and potentially improve its current accountability processes at multiple levels of the increasingly decentralized Nepalese education system [4]. To achieve this aim we will: (1) develop a variety of accountability assessment tools for use in Nepal's education system; (2) modify a set of instructional processes and instructional quality measures developed for use in OECD countries for use in the Nepali educational system; and (3) gather data on students' academic achievement using standardized test items developed by Nepal's MOE. Aim Two: To Investigate How Accountability Processes; Environments for Student Learning in Schools, Families, and Communities; and Student Learning are Related. This involves investigating three main research questions: Are accountability processes systematically related to socioeconomic disparities among communities, schools within communities, and families within schools? In school and community settings where accountability processes are more intensive, is the quality of instructional service delivery higher? And, controlling for socioeconomic disparities related to student achievement is student learning higher in schools and communities where accountability processes are more intensive? To meet this aim, we will: (1) administer a newly designed PET-QSDS survey to 380 key stakeholders; (2) administer the NASA test at the beginning and end of the school year and a student survey to 1,740 8th graders; and (3) administer a teacher survey to 1,392 teachers and a parent survey to 1,740 parents. The results of this research will be relevant to education policy makers in Nepal and will also contribute directly to comparative education research on school effectiveness. This study will generate rigorous scientific outcomes: (1) development of a low income context adaptive accountability assessment tool; (2) cross-cultural assessment of the reliability and predictive validity of accountability measures; (3) identification of contextual factors with strong correlation with accountability; (4) potential for identification of new dimensions of accountability in low income settings; and (5) scientific advancement in our understanding of the relationship between accountability, instructional quality and students' gains in achievement. These outcomes will be made widely available to scientists and policy makers. First, we will conduct dissemination workshops at local and national levels to share findings of the study and provide training on the use of the newly designed accountability assessment tool and analysis of the data generated through the various surveys mentioned above. Second, the data will be made available through ICPSR and the UK Data Service. Third, the findings will be disseminated through presentations at national and international conferences and published in scientific articles, and research and policy briefs. Finally, the participation of Nepali faculty, scientists, government representatives and school authorities throughout the project will advance the scientific and analytical capacity of their respective host institutions (DOE,TU, PABSON, PDs).