11. Educational Improvement and Quality Assurance
Paper
Input Stratification? The Case of Tracking & School Resources
Maximilian Brinkmann, Nakia El-Sayed, Janna Teltemann
University of Hildesheim, Germany
Presenting Author: Brinkmann, Maximilian
Early between school tracking (i.e. tracking) is an institutional set-up that has strong implications for students and their future outcomes. While there is increasing consensus that tracking does not increase average levels of achievement but average levels of inequality (e.g., Terrin & Triventi 2022, meta-analysis), we know little about the underlying mechanisms. The focus of this study is a potential mechanism that is as prominent as it is understudied: the role of unequal school resources in tracked systems. In other words, while higher tracks (i.e. academic tracks) benefit from above-average resources, lower tracks (i.e. vocational tracks) are disadvantaged. We call such a situation input-stratification, because the total input (resources) assigned to the education system is stratified across tracks (c.p. Esser 2016).
Input stratification has been discussed as a potential mechanism throughout the literature (e.g. Betts 2011; van de Werfhorst 2021, Terrin & Triventi 2022), but there are hardly any studies providing evidence on the unequal distribution of resources across tracks. For instance, van de Werfhorst (2021; 1214) and Terrin & Triventi (2020, meta-analysis; 7) still cite the prominent paper of Brunello & Checchi (2007). While Brunello & Checchi (2007; 795) only provide student-teacher ratios in 2004 for a handful of countries, ostensibly because resources are not the focus of their study, they apparently provide the best evidence on input-stratification to this date. The scarcity of evidence may be explained by the inherent difficulty of estimating (causal) effects of school resources (e.g. Gibbons & McNally 2013) combined with the scarcity of high-quality data that allows for comparison across countries. Thus, a consequence is that we do not know whether a) input stratification (unequal resources) actually exists in tracked systems and b) whether it drives unequal levels of achievement in different tracks. Although answering b) is outside the scope of this study, we argue that it is still valuable to know about a) since b) presupposes a). Our approach is driven by two goals.
First, we want to provide a thorough discussion on the role of school resources in tracked systems. While the topic of school resources is often mentioned in the literature, it is hardly spelled out with its theoretical implications. The unequal distribution of resources across tracks could be a mechanism that explains why tracked systems fail to show increased (average) achievement but increased social inequalities (i.e. Terrin & Triventi 2022). Accordingly, students on higher (lower) tracks benefit (are disadvantaged) through resources above (below) average. But since tracks are segregated by social status (Strello et al. 2022), high (low) status students are more (less) likely to benefit from above-average resources, explaining the increased social inequality in tracked systems.
Given the limited space, we merely note that we incorporate the existing literature on school resources in our study (e.g. Krüger 2003 vs. Hanushek 2003). We distinguish between explicit resources (e.g. official government funding) and implicit resources which are indirect consequences of the institutional set-up (e.g. self-selection of more capable or motivated teachers). Further we discuss whether differences in resource allocation across tracks should be seen as a bug or a feature of tracked systems (i.e. vocational vs. academic training; c.p. Esser 2021).
Second, we want to assemble data sources that are either informative about resource levels 1) across tracks within a country or 2) across tracked and untracked countries. Noting the inherent difficulties of estimating the effects of school resources, we will restrict our analysis to a descriptive analysis in order to answer the question whether the existence of input-stratification is plausible given the existing evidence.
Methodology, Methods, Research Instruments or Sources UsedResearch plan:
In general we want to restrict our analysis to a thorough descriptive analysis of tracked (and untracked) education systems. Our analysis comprises two parts: First, using administrative data from the German speaking tracking countries (Germany, Austria, Switzerland, Luxembourg) we will discuss educational spending per student, teacher-student ratios, teaching hours and teacher qualifications across tracks in a case-study like fashion. Second, for our main analysis we will use large-scale assessment data (LSA) from the last 25 years.
Unfortunately, measures of resources in PIRLS & TIMSS 4 (primary school) and PISA (secondary school) are largely incomparable which hampers efforts to compare change over time from primary to secondary school. However, it is possible to compute teacher-student ratios from LSA data (e.g. Woessmann & West 2006), which is a resource indicator commonly used in the school resource literature (e.g. Gibbons & McNally 2013). This allows us to track the change in the variance of teacher-student ratios in tracked and untracked countries as they move from primary (no country is tracked) to secondary school (some countries have administered tracking).
Lastly we will use PISA data to compute a broader set of resource indicators which resonates with our idea of explicit and implicit resources. Using PISA has the advantage that we can draw on a broad set of variables and that we can directly identify the school track (as compared to TIMSS 8). Unfortunately, however, PISA has only administered (short) teacher questionnaires since 2015. To remedy this shortcoming, we aim to match PISA with TALIS data, which provides in-depth data on the teacher and school principal level. Taken together, this allows us to compute different indicators of explicit and implicit school resources (e.g. material resources, student-teacher ratios, teacher qualifications, teacher motivation, parental support) across track types.
Conclusions, Expected Outcomes or FindingsWe are still in the process of data collection (cleaning & assembling). The limited evidence on school resources indicates large differences in student-teacher ratios in five tracked systems (Brunello & Checchi, 2007). However, Brunello & Checchi also report administrative data from Austria that shows that spending per student is higher in vocational tracks.
Overall, we expect more mixed evidence when it comes to explicit resources (i.e. official allocation of resources through the government) as compared to implicit resources (i.e. differences in resources as a consequence of the institutional set-up). Tracking is often understood as a form of stratification, inducing an implicit “better” or “worse” into the system. Further, it is theoretically plausible (e.g. Boudon 1974) and empirically validated (Strello et al 2022) that tracked systems are segregated by social status. We argue that this could lead to differences in implicit resources because involved actors take this stratification and segregation into account. More able or motivated teachers could self-select into higher tracks, parental support via booster clubs is likely to depend on the average social status of parents at the school and so on.
ReferencesBetts, J. R. (2011). The economics of tracking in education. In Handbook of the Economics of Education (Vol. 3, pp. 341-381). Elsevier.
Boudon, R. (1974). Education, opportunity, and social inequality: Changing prospects in western society.
Brunello, G., & Checchi, D. (2007). Does school tracking affect equality of opportunity? New international evidence. Economic policy, 22(52), 782-861.
Esser, H. (2016). Bildungssysteme und ethnische Bildungsungleichheiten. Ethnische Ungleichheiten im Bildungsverlauf: Mechanismen, Befunde, Debatten, 331-396.
[English: “Education systems and ethnic educational inequalities” in “Ethnic inequality along the educational pathway: Mechanisms, Results, Debates”]
Esser, H. (2021). » Wie kaum in einem anderen Land...«?: Die Differenzierung der Bildungswege nach Fähigkeiten und Leistungen und ihre Wirkung auf Bildungserfolg,-ungleichheit und-gerechtigkeit. Band 1: Theoretische Grundlagen. Campus Verlag.
[English: “‘Hardly any other country…’?: Differentiation of educational pathways according to aptitude and performance and their consequences for educational attainment, inequality and justice. Volume one: Theoretical foundations” ]
Gibbons, S., & McNally, S. (2013). The effects of resources across school phases: A summary of recent evidence.
Hanushek, E. A. (2003). The failure of input‐based schooling policies. The economic journal, 113(485), F64-F98.
Krueger, A. B. (2003). Economic considerations and class size. The economic journal, 113(485), F34-F63.
Strello, A., Strietholt, R., & Steinmann, I. (2022). Does tracking increase segregation? International evidence on the effects of between-school tracking on social segregation across schools. Research in Social Stratification and Mobility, 78, 100689.
Terrin, E., & Triventi, M. (2022). The effect of school tracking on student achievement and inequality: A meta-analysis. Review of Educational Research, 00346543221100850.
van de Werfhorst, H. G. (2021). Sorting or mixing? Multi‐track and single‐track schools and social inequalities in a differentiated educational system. British Educational Research Journal, 47(5), 1209-1236.
Woessmann, L., & West, M. (2006). Class-size effects in school systems around the world: Evidence from between-grade variation in TIMSS. European Economic Review, 50(3), 695-736.
11. Educational Improvement and Quality Assurance
Paper
What Data to Use for Planning Educational Reforms? A Meta-analysis of Educational Interventions' Research in post-Soviet Countries
Roman Zviagintsev1, Julia Kersha2
1University of Vienna; 2HSE University
Presenting Author: Zviagintsev, Roman;
Kersha, Julia
The education system is a key social institution of any modern state, critical to the socio-economic and cultural development of society. For this institution to work effectively in a complex and uncertain world, its management must be data-driven (Burns et al., 2016). Informed transformation of educational content and the systematic use of new technologies in pedagogical practice are important characteristics of effective educational systems (Nelson & Campbell, 2017; Wiseman, 2010). Only research can determine "what works," similar to what is successfully implemented in, for example, health care (Davies, 1999).
The lack of effective use of research in educational policy and practice is regularly highlighted, and mechanisms are sought to strengthen the impact of research on decision-making in education systems (OECD,2022). The importance of and need for evidence-based decision-making was particularly vivid during the pandemic, highlighting how the existing gap between research and policy decisions may have dramatic consequences (Stuart & Dowdy, 2021).
A fairly wide pool of interventions designed to increase educational outcomes exists and well documented in international research. Prominent examples of cataloging this type of information are, for example, the following electronic resources:
- The U.S. Department of Education's Institute of Educational Sciences WWC open repository[1];
- repository of practices to help Every Student Succeeds Act, Evidence for ESSA[2];
- BEE Encyclopaedia of Educational Practices, created by Johns Hopkins University[3].
Webpages such as these appear due to the fallacy of the view that any educational projects are useful and effective. Moreover, evaluations of their effectiveness are often highly contradictory. It is not uncommon to find that proposed interventions do not lead to any worthwhile outcome (Lortie-Forgues & Inglis, 2019). However, the very fact that educational programs are evaluated for their effectiveness is extremely important in optimizing resources for their implementation and scaling.
The movement toward evidence-based education is most evident in English-speaking countries (Dekker & Meeter, 2022). However, there is growing interest in China (Slavin et al., 2021), Sweden (Segerholm et al., 2022), the Netherlands (Wubbels & van Tartwijk, 2017), Italy (Mincu & Romiti, 2022), France (Bressoux et al., 2019)[4]. To a much lesser extent, we see the development of this movement in the Post-Soviet states. The importance of the studies has been only increasing and is especially evident since these countries began to participate in PISA, the results of the first waves of which opened up possibilities for analyzing the connection between differences in the quality of educational outcomes and the reforms implemented (Khavenson & Carnoy, 2016).
Of particular interest is the ability of countries to generate and use their own "contextualized" research data about "what works" in education. Here one encounters a problem — in general, there are few works characterizing the landscape of educational research in the post-Soviet space (Chankseliani, 2017; Hernández-Torrano et al., 2021). We found no systematic review or meta-analysis of publications summarizing research on the impact of educational interventions on school students' academic outcomes that could allow policymakers and practitioners to construct informed educational policies.
The purpose of our paper is to evaluate the effectiveness of educational improvement programs in the Post-Soviet states, as there is a clear lack of systematization of such information in the presence of a clear demand for data-driven reforms. Our paper simultaneously addresses two tasks leading to the overall goal — a meta-analysis of studies summarizing the experience of post-Soviet countries in terms of interventions aimed at improving educational outcomes, and the search for a basis for building educational policies that would consider the current state of affairs.
[1] https://ies.ed.gov/ncee/wwc/
[2] https://www.evidenceforessa.org/page/about
[3] https://bestevidence.org/
[4] References are omitted in the list due to the lack of space.
Methodology, Methods, Research Instruments or Sources UsedTo answer the research question meta-analysis methodology was used. While conducting meta-analysis, we carefully followed the PRISMA statement guidelines (Page et al., 2021). To create the database, we used the following selection criteria:
• the study must be devoted to evaluating the effectiveness of the program aimed to improve the academic achievements of schoolchildren or develop their skills facilitating successful learning;
• the study should be conducted in the form of an experiment (the presence of randomization procedure in the study was coded, but was not a strict exclusion criterion);
• schoolchildren must be the general population of the study;
• the study is conducted on the sample of schoolchildren from one of the 15 post-Soviet countries;
• the text is published in Russian or English;
• the research must be a published article in a peer-reviewed journal or a defended PhD thesis;
• year of publication from 1992 to 2023.
To achieve the research goal, we systematically searched in these 4 scientific databases: Scopus, Web of Science, ProQuest, Google Scholar (the search was conducted in September 2022) . We were looking for the keywords that characterize the study design (experiment, control, rct etc.) and the dependent variable (achievement, performance, learning outcomes etc.). A total number of the publications suitable for analysis was 262. All of them were screened using Rayyan software . At the last step, we selected 27 papers for the full-text analysis. Three members of the research group went through the procedure of coding. The key parameters recognized in the coding scheme were: authors, year, type of publication, country, sample characteristics, sample size, dependent variable, type of the intervention, duration of the intervention, presence of randomization.
We chose Cohen's d as the key statistic for an effect size calculation. We used an online calculator to convert the statistics published in studies to d. Afterwards, we carried out a classical version of the statistical meta-analysis with the random effects model in JASP . In addition, we assessed the homogeneity of the studies and checked whether the results demonstrate any kind of publication bias. To estimate the bias, we used the graphical method and the Egger’s test, as well as the selection models.
Conclusions, Expected Outcomes or FindingsOur final results are based on 28 effect sizes from 27 publications with a total sample of 14,853 schoolchildren. According to the age of the respondents, the studies covered samples from grades 1 to 9. It should be noted that there were no papers with high school students as the general population. Regarding the program type, vast majority of them were classified as pedagogical technologies. The overall mean effect size of the studies is 0.48 with a 95% confidence interval of 0.36 to 0.60 (and a range of 0.02-1.55). At the same time, we see that the effect size varies greatly across the studies — indicator of heterogeneity equals 82% (i2). If we consider three studies in which proper randomization was carried out, the effect size of the interventions decreases to 0.07 and becomes insignificant.
We are going to build our discussion around the particular limitations and general barriers one the way of carrying high quality research. For example, we can conclude from the available research that in those countries all along there were and are: difficulties in accessing data when conducting research (Jonbekova, 2020); specifics of research culture and methodology, especially experimental research (Gromyko & Davydov, 1998); problems with standards of reviewing, publishing, academic integrity (Kuzhabekova & Mukhamejanova, 2017), a general low level of integration into international science.
It is important to note that the idea of "what works" is only possible in a situation where the goals of the education system are clear (Hammersley, 2005), but many countries were dealing with much more severe issues since the collapse of the USSR. Politicians' words about the need for research are often just a blind "fashion" following. The very statement “we need data-driven policy” in a situation where there are almost no data is, at the very least, deceitful.
ReferencesBurns, T., Köster, F., & Fuster, M. (2016). Education Governance in Action. OECD. https://doi.org/10.1787/9789264262829-en
Chankseliani, M. (2017). Charting the development of knowledge on Soviet and post-Soviet education through the pages of comparative and international education journals. Comparative Education, 53(2), 265–283. https://doi.org/10.1080/03050068.2017.1293407
Davies, P. (1999). What is Evidence-based Education? British Journal of Educational Studies, 47(2), 108–121. https://doi.org/10.1111/1467-8527.00106
Dekker, I., & Meeter, M. (2022). Evidence-based education: Objections and future directions. Frontiers in Education, 7. https://doi.org/10.3389/feduc.2022.941410
Gromyko, Iu. V., & Davydov, V. V. (1998). The Conception of Experimental Work in Education Ideas for a formative experiment. Journal of Russian & East European Psychology, 36(4), 72–82. https://doi.org/10.2753/RPO1061-0405360472
Hernández-Torrano, D., Karabassova, L., Izekenova, Z., & Courtney, M. G. R. (2021). Mapping education research in post-Soviet countries: A bibliometric analysis. International Journal of Educational Development, 87, 102502. https://doi.org/10.1016/j.ijedudev.2021.102502
Jonbekova, D. (2020). Educational research in Central Asia: methodological and ethical dilemmas in Kazakhstan, Kyrgyzstan and Tajikistan. Compare: A Journal of Comparative and International Education, 50(3), 352–370. https://doi.org/10.1080/03057925.2018.1511371
Khavenson, T., & Carnoy, M. (2016). The unintended and intended academic consequences of educational reforms: the cases of Post-Soviet Estonia, Latvia and Russia. Oxford Review of Education, 42(2), 178–199. https://doi.org/10.1080/03054985.2016.1157063
Kuzhabekova, A., & Mukhamejanova, D. (2017). Productive researchers in countries with limited research capacity. Studies in Graduate and Postdoctoral Education, 8(1), 30–47. https://doi.org/10.1108/SGPE-08-2016-0018
Lortie-Forgues, H., & Inglis, M. (2019). Rigorous Large-Scale Educational RCTs Are Often Uninformative: Should We Be Concerned? Educational Researcher, 48(3), 158–166. https://doi.org/10.3102/0013189X19832850
Nelson, J., & Campbell, C. (2017). Evidence-informed practice in education: meanings and applications. Educational Research, 59(2), 127–135. https://doi.org/10.1080/00131881.2017.1314115
OECD. (2022). Who Cares about Using Education Research in Policy and Practice? OECD. https://doi.org/10.1787/d7ff793d-en
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, n71. https://doi.org/10.1136/bmj.n71
Stuart, E. A., & Dowdy, D. W. (2021). Evidence-based COVID-19 policy-making in schools. Nature Medicine, 27(12), 2078–2079. https://doi.org/10.1038/s41591-021-01585-2
Wiseman, A. W. (2010). The Uses of Evidence for Educational Policymaking: Global Contexts and International Trends. Review of Research in Education, 34(1), 1–24. https://doi.org/10.3102/0091732X09350472
11. Educational Improvement and Quality Assurance
Paper
An Explanatory Quantitative Study of the Funding Policy Supporting British Academies and American Charter Schools
Tyrone Bynoe
St. Bonaventure University, United States of America
Presenting Author: Bynoe, Tyrone
Choice theory has shaped much of public-school policy throughout the world as countries have adopted free market principles to establish educational alternatives to state and traditional public schools with the goal of improving chronically failing schools. While public dollars are being used globally to support private school enterprises, public dollars are also being used to finance public school alternatives to the state and traditional public-school offerings. On both sides of the Atlantic Ocean during the 1990s, school choice theory promoted public school alternatives to state or traditional public schools with the emergence of Multi-Academy Trusts in the United Kingdom and the rise of Charter Schools in the United States. Beginning with the Education Reform Act of 1988 -- which allowed for the formation of city technical academies -- and since the “academisation” of schools in 2010, the number of multi-academy trusts have soared from 407 in 2011 to 6493 during 2017 (Male). Concurrently since the first charter school in Minnesota in 1992, the number of charter schools in the United States have climbed to 7,038, and 44 states now have charter school laws. Both school growth trends in choice schools have had the unflinching endorsement of respective national governments.
Theoretical Framework: Choice Theory is the study’s theoretical framework. Choice theory is the antithesis of the governmental centralization of public schools, as conceived by German fiscal influence in the United States educational policy during the early twentieth century (Seligman). Choice Theory seeks equitable and efficient resource distribution through parental selection and market-fueled competition. Whether promoted by Milton Friedman in the 1950s (Friedman) or Adam Smith during the 1770s, Choice Theory application advocates a variety of resource distribution schemes in contemporary school finance policy, featuring tuition tax credits, vouchers, funding portability, and charter schools. Given this framework, my study will evaluate how the theoretical framework has effectively or ineffectively been implemented in school finance policy within British multi-academy trusts and US charter schools through an equity and efficiency analysis. Due to the researcher’s limitations to aggregate data on all multi-academy trusts and charter schools in both respective countries, an explanatory quantitative study will be conducted among choice schools in these countries’ largest municipalities: London (or Inner London - its schools within fourteen local education authorities) and New York City (NYC’s Department of Education). Given this theoretical backdrop, the paper’s main research questions include:
1. To what extent do the distribution of per pupil expenditures of London multi-academy trusts and New York City (NYC) charter schools vary when compared to the distribution of these per pupil expenditures in state schools and the traditional public schools in each of these municipalities?
2. To what extent does the per pupil funding formula allocate to the magnitude of student need in both London multi-academy trusts and NYC charter schools when compared to this needs-based allocation in the traditional public and state schools in each of these municipalities?
3. Given the school spending data from the Income and Expenditure Reports in England for state and choice schools and the School Based Expenditure Reports in the NYC public schools, to what extent can one track the usage of resources to determine what percentage of resources are being allocated for total uses on instruction – minus capital outlays, security, transportation, building up-keep, and other non-instructional needs?
4. Are multi-academy trusts and charter schools respectively in London and NYC getting more bang for the “pound” or “buck” when analyzing the relationship between student gain scores and per pupil expenditures in these schools, especially when compared to analyzing the same data of state and traditional schools in these two municipalities?
Methodology, Methods, Research Instruments or Sources UsedMethods:
Question 1 will use descriptive statistics and school-finance equity measures to analyze the spending variability in different parts of the distribution for each set of schools. Question 2 will use an ordinary least square regression to determine if the unstandardized beta coefficients, partial correlations and effect-sizes of this statistical model reinforce or refute real allocation of need based equalization policies in both sets of schools within both municipalities. Question 3 will use a functional analysis model, which will track spending at various decentralized levels of schooling in each set of schools within the respective municipalities to analyze the extent the spending is being allocation to instruction. Question 4 will derive effective size from hypothesis testing and correlations to evaluate the efficiency of the funding policy as a function of student gain scores.
The study’s unit of analysis is at the school level, and subsequently embraces the new school-finance perspective, which calls for more meaningful input-output analysis at the school level (Grubb & Huerta). School finance data will be aggregated at the pupil level for the years 2011-2012 to 2016-2017. The British spending data will be collected from the British Department of Education’s (DfE’s) Local Authority and School Expenditure reports. This specific British per pupil school finance data source contains revenue and expenditure data on the primary and secondary state schools of Inner London’s fourteen local education authorities. From this same agency, per pupil school finance data on multi-academy trusts will be aggregated from the Income and Expenditure Reports in England for only years 2011-2012 to 2016-2017, focusing only on financial data of multi-academy trusts in the 14 Inner London local education authorities. The NYC school finance data will be aggregated from its Education Department’s School Based Expenditure Reports, which contains rich per pupil finance data on all NYC public schools, including charter schools, by school and function respectively. All data will be reported in Excel spreadsheets, and then transported to SPSS for statistical analysis. British school level student performance data will originate from its accountability program (i.e. A-level exams of secondary schools), and NYC school-level student performance will be aggregated from the publicly available state accountability data system.
Conclusions, Expected Outcomes or FindingsAnticipated findings: It is hypothesized that expenditures patterns will vary between choice schools and state/traditional schools in respective Inner London and NYC municipalities using horizontal and vertical equity measures. Horizontal equity measures will include the coefficient of variation (measuring spending variability within the middle 68 percent of the distribution), McLoone Index (measuring spending variability under the median), Verstegen Index (measuring spending variability above the median) as well as other measures.
Secondly, vertical equity measures will generate findings featuring an interpretation of the beta coefficients of an ordinary least squared regression to evaluate whether the actual funding levels of the need equalization formula are being allocated according to the formula’s design.
Thirdly, it is anticipated that some measure of decentralized resource allocation will be found when using the functional analysis model to track funding at specific levels of schooling.
Additionally, analyses between spending and student performance will either validate evidence toward trends of economies of scale and efficiency or show evidence for a diseconomies of scale and inefficiency.
ReferencesReferences
Bynoe, T. (2018). A historical and conceptual overview of school-finance equalization models – a book chapter. In BenDavid-Hadar, I. (eds). Education, equity and economy. New York, NY: Springer, Inc
Bynoe, T. & Armstead, A. (2019). American charter schools and British academy trusts: An comparative perspective on the school choice movement since the 1990s. In Storey, V. (ed.). Pathways to school leadership: Negotiating context and diversity in England and the United States. Charlotte, NC: Information Age Publishing, Inc (In-Press).
Bynoe, T. & Feil, J. (November, 2016). School finance equity: Lessons learned from Michigan’s charter school spending patterns, School Business Affairs Journal. 82(10), 19-22.
Department for Education. (2016). Educational excellence everywhere: Presented to Parliament by the secretary of education by command of her Majesty. (Cm 9230). Retrieved from: http://www.educationengland.org.uk/documents/pdfs/2016-white-paper.pdf
Friedman, M. (1955). The role of government in education. Retrieve from: http://homepage.fudan.edu.cn/jfeng/files/2011/08/role-of-government-in-education_Friedman.pdf
Grubb, W.N. & Huerta, L.A. (2001). Straw into gold, resources into results: Spinning out the implications of the improved school finance. Journal of Education Finance, 31(14), 334-359.
Hoxby, C. M. (2003). School choice and school productivity: Could school choice be a tide that lifts all boats? In C.M. Hoxby (Ed.), The economics of school choice (pp. 287-341). Chicago, IL: University of Chicago Press.
Ladd, H. & Fiske, E. (November 2016). Report: Lessons for US charter schools from the growth of academies in England. Brookings. Retrieved from: https://www.brookings.edu/research/lessons-for-us-charter-schools-from-the-growth-of-academies-in-england/
Male, T. (September, 2017). Multi-academic trusts: A background briefing paper. London, United Kingdom: The London Centre for Leadership in Learning. Retrieved from: http://www.lcll.org.uk/uploads/2/1/4/7/21470046/2017_multi-academy_trusts_-_a_background_briefing_paper_-_trevor_male.pdf
Seligman, E. (December 1908). Progressive taxation in theory and practice, 3rd Series, American Economic Association Quarterly. 9(4).
West, A. (2015). Education policy and governance in England under the coalition government (2010-15): Academies, the pupil premium and free early education. London Review of Education, 13, 21-36.
West, A. & Wolfe, D. (2018). Academies, the school system in England and a vision for the future: Clare Market Papers No. 23. London, England: Education Research Group-Department of Social Policy-London School of Economics and Political Science. Retrieved from: http://www.lse.ac.uk/social-policy/Assets/Documents/PDF/Research-reports/Academies-Vision-Report.pdf
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