School Resources, Quality and Quantity of Schooling and Cognitive Development

There is a longstanding debate regarding the importance of school factors on children’s educational outcomes. Whilst early work (for example “Equality of Educational Opportunity”, more commonly known as the Coleman Report, 1966) indicated that the influence of family background and cohort factors far outweighed the effect of schools, later papers have found that schools are in fact extremely important (Hanushek, 1986, 2003, 2005).

Apart from the issue of self-selection into schools, or moreover, parental selection of schools, which conflates family and school effects, another issue is the difficulty in measuring school and teacher quality. In terms of teaching quality, studies which have focused on measures such as years of experience and qualifications have found that these effects are in general insignificant (e.g. Todd and Wolpin, 2007). However, empirical work that examines teacher characteristics more broadly, using teacher fixed

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effects for example, finds that differences between teachers have an important effect on pupil’s outcomes (e.g. Rivkin et al, 2005). Card and Krueger (1998), reviewing literature on the effect of schooling on earnings later in life and educational attainment, concluded that it was “unfortunate and frustrating” that not more was known about the outcomes of schooling, even 30 years after the Coleman report was produced. There still remains a great deal of ambiguity as to the strength and mechanisms of the effect of schools and teachers on pupils’ outcomes.

A recent study (Holmlund et al, 2010) makes use of excellent UK data to control for school fixed effects as well as detailed individual characteristics. They examine the effect of rising school expenditure on children’s test scores at age 11 and find that school expenditure has a consistently positive and significant effect and that this effect is higher for students who are economically disadvantaged. On the other hand, Todd and Wolpin (2007), which examines a broad range of specifications and employs various controls, does not find any significant relationship between the schooling input measures and test scores, but rather finds that the key contributors to ethnic test score gaps are mother’s “ability” (as measured by AFQT scores16) and home inputs. The evidence on this issue certainly remains mixed.

Baird (2012) introduces a further element by investigating achievement gaps (between high and low SES background pupils) for 19 high-income countries and finds that in some countries achievement gaps can be largely explained by differences in the characteristics of schools attended, whilst in many other countries, the gap appears more closely related to differences in the characteristics of the students. This finding seems to indicate that broader institutional factors also have an important role to play.

Hanushek (1986) discusses the distinction between the overall influence of a child’s school and teachers and the influence of specific components of this such as average school expenditure and years of teacher experience. Using teacher fixed effects, he finds that teachers and

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schools differ dramatically in their effectiveness; however, this is not well reflected in traditionally measured components. He argues that existing measures, including school expenditure, class size, salary levels, teacher experience and whether the teacher has a master’s degree, are flawed measures of true school quality.

This is an argument he has developed further over a long time period, for example in Rivkin, Hanushek and Kain (2005) where the authors use a large and detailed Texan dataset to identify the effect of teacher quality explicitly. Their estimator is based on patterns of within-school variation in achievement gains and ignores differences in teacher quality between schools, which cannot easily be disentangled from student differences and the influence of other school factors. Rich data with repeated performance observations for individual students and multiple cohorts makes it possible to use fixed effects models, thereby providing a means of controlling explicitly for student heterogeneity and the non- random matching of students, teachers, and schools. Their paper uses excellent data to provide robust evidence for the abovementioned finding, namely that schools and teachers do matter for students’ achievement, but that their effectiveness is not well measured through standard variables such as school expenditure and teacher experience. One outcome of this research is that it has led to calls for different incentive structures within schools which will be more effective at identifying and rewarding effective teachers, such as rewards based on head teacher reports, which would provide a more comprehensive perspective (Hanushek, 2003).

A further approach taken in the recent literature is to employ instrumental variables to identify causal impacts of certain school characteristics. Papers using this approach aim to overcome endogeneity problems by isolating a credible source of exogenous variation in school inputs, and are often quite innovative in their reasoning. For example, Haegeland et al (2012) examines the influence of school resources in Norway using variation that is induced by proximity to waterfalls. The waterfalls lead to higher local tax revenues from hydropower plants and this leads to higher school expenditures in those areas. Simple OLS regressions show insignificant effects of school expenditure on outcomes at

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age 16. This may be due to compensation of disadvantaged schools by local authorities, which is likely to bias the effect of school resources downwards. Using an IV approach helps to overcome this issue. The authors run two-stage IV regressions on the whole sample and also on a restricted sample of “comparable” municipalities and furthermore perform several robustness checks to explore possible biases arising from selective mobility into these areas or the influence of other local amenities. They find an economically and statistically significant positive effect of school expenditures using the IV approach.

Possibly the best known paper using this approach is Angrist and Lavy (1999) examining class size effects. Class size is one element of school quality for which it is particularly difficult to determine a causal effect, given that children with particular needs may often be placed in smaller classes, and on the other hand that there is a strong association class size and the pupils’ family background. In this paper, the authors use data from Israeli schools where the application of a particular rule (Maimonides rule, which states that 40 is the maximum possible number of students in a class) prompts a discontinuity in school class sizes. They note a clear pattern of up-and-down test scores that correlates strongly with the class size pattern induced by the application of this rule. Their research indicates clear, positive effects of smaller class sizes, though in comparison to work on the Tennessee STAR experiment (a randomised trial designed explicitly to measure class size effects), the effects were somewhat smaller. Other papers (e.g. Rivkin et al, 2005) have argued that reducing class sizes is a particularly expensive way of improving children’s educational experience and that the effects are small relative to other possible measures such as improving teacher quality.

Another element of school quality is whether the school is coeducational or single-sex. Whilst research has shown that pupils (both girls and boys) in single-sex schools perform better (e.g. Lee and Bryk, 1986), this could merely reflect student selection into school types

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(Jackson, 2012)17. Park et al (2013) try to identify a causal effect of school type using data on schools in Seoul, South Korea, where a compulsory random allocation into schools ensures that attendance of a coeducational or single-sex school is unrelated to a pupil’s family background and other characteristics. This research admittedly focuses on secondary school, with the two main outcome measures being the nationally standardised college entrance examination and attendance of four-year rather than two-year colleges; however, it is still informative regarding the effect of coeducational and single-sex schools more generally. The authors find a significant positive effect of single-school attendance on college entrance scores and college attendance for both boys and girls.

Finally, it is also possible to use natural experiment techniques to explore the effect of the quantity of schooling on children’s outcomes. Marcotte (2007) uses snowfall in Maryland in the US as an instrument for days of school attended in a school year. The Maryland School Performance Assessment Program tests are held in the same week each year, but days lost to increment weather vary substantially by school district and by year. This provides a source of ransom and non-trivial variation in instructional time which can be exploited to determine a causal relationship between schooling and achievement. Marcotte’s finding was that there was a substantial effect of instructional days on test scores, and that this was stronger for mathematics compared to other subjects and for lower grades compared to higher grades. Secondly, Carlsson et al (2012) uses random variation in test dates for a Swedish military preparation exam. They find that school days have a positive effect on crystallized intelligence tests (synonym and technical comprehension tests) while non-school days have no effect, but that school days have no effect on fluid intelligence tests (spatial and logic tests). These two papers provide evidence on the importance of instruction days as an input into the educational production process.

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Jackson (2012) also uses rule-based assignment to schools to identify a causal effect of single-sex schools, but the identification is less strong than in Park et al (2013), hence my focus on the second paper.

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The key issue of the endogeneity of school inputs has continued to prove very difficult to resolve. The fact that families have so much influence on school choice and also exert a strong influence on children’s academic outcomes means separating out the direct effect of schools has continued to prove difficult since the original Coleman report was produced in 1966. The review above briefly introduced two approaches that have been used to deal with this, namely fixed effects models and natural experiment (instrumental variable) techniques. My own results in this chapter are strengthened by the use of individual panel data models, although data limitations restrict what is possible in terms of teacher and school fixed effects. This will be discussed further in the data and methodology sections. My research contributes to this large body of literature by using a rich, current dataset to explore the effect of school and teacher characteristics in the UK alongside a vast array of other factors including family income, neighbourhood characteristics, parental education, labour force engagement and behaviours, family structure, the child’s own characteristics and birth-related factors.

4.2.3 Papers Measuring the Impact of Specific Child, Family and