# **<mark>3.2. Establishing Causality in Educational Research</mark>**

<mark>Establishing causal relationships between education and later life outcomes such as incomes, employability, and even voting behavior has been of interest for many decades. While there is overwhelming evidence for the positive impact that schooling can have, researchers have been cautious in drawing strong inferences about the causal effect of schooling especially in the absence</mark> of experimental evidence.

<mark>However, the emergence of large-scale microeconomic datasets such as OECD’s Programme for International Student Assessment (PISA), Trends in International Mathematics and Science Study (TIMSS), and Progress in International Reading Literacy Study (PIRLS) has provided researchers with more tools to study these relationships. It is now possible to deploy econometric methods such as instrumental variables, regression-discontinuity, propensity score matching, difference-indifference, and different sorts of fixed-effects specifications to establish causality. Cordero and Cristóbal (2017) provide a comprehensive overview of literature that uses such quasi-experimental techniques. The authors also provide examples of studies that have used such techniques to establish the impact of various school education policies. Particularly, the difference-in-difference approach and instrument variables strategy have been most frequently used for comparison between public and private schools, or to study the effects of class size, tracking, instructional time, teaching methods, school entry age, etc. The authors further suggest creating longitudinal datasets to further causal research in the sector. Schlotter et al. (2011) provide further examples</mark> for the applications of these causal methods and the issues faced in aggregation of relevant data.

<mark>Other techniques that have been suggested include using co-twin control designs on many monozygotic twin pairs to understand the impact of schooling on factors such as political knowledge (Weinschenk and Dawes 2019), wages (Bingley et al. 2009), and health (Fujiwara and Kawachi 2009). Heckman et al. (2016) and Card (1999) further present theoretical models that build on variations of the simple static models presented by Becker (1964) to estimate the private returns to education. However, estimating the social returns using these models remains a</mark> limitation.

<mark>Thus, while numerous methods have been advanced to causally estimate the ex-post returns for education, the lack of large-scale panel data limits generalizability of these results. Using compulsory schooling laws as a source of exogenous variation is thus one possible way to overcome this limitation especially because many countries have historically introduced or made changes to their compulsory schooling laws at different times. The next sub-section presents an overview of some studies that make use of these laws to establish causality.</mark>

# **<mark>3.3. Compulsory Schooling as an Instrument and Its Use in Climate Research</mark>**

<mark>Numerous studies have used changes in compulsory schooling laws, where some individuals are randomly forced to stay in school longer than their peers, as a natural experiment to research the effect of educational attainment on various aspects of human development. For example, Angrist and Krueger (1991), using longitudinal data from the U.S. Censuses of 1960, 1970, and 1980 for men show that compulsory school attendance laws can have a positive effect on schooling and earnings (see also Domnisoru 2021). The authors further suggest that compulsory schooling laws</mark> <mark>9</mark>