Common Contributions

While the three main chapters of this dissertation make independent contributions to the economics of education literature, there are a number of recurrent themes that link the arti- cles and can therefore be considered as common contributions.

The first and central common contribution of the three chapters is their focus on the

formation of cognitive skills. More precisely, all chapters deal with cognitive skill returns to interventions in education systems. Cognitive skill acquisition can be classified as an intan- gible effect of education in the sense that such skills neither entail any direct monetary re- wards nor make a direct statement about success on the labor market (see e.g. Dahmann, 2016). Since economists have traditionally been interested in tangible, monetary outcomes of education, the literature on cognitive skill returns is still quite patchy and leaves a num- ber of research questions unanswered. This dissertation is dedicated to three of them, name- ly the long-run effects of center-based child care attendance, the short-term effects of apply- ing potentially engaging teaching practices in primary school, and the short-term effects of class size in primary school. By focusing on such a diverse array of interventions that in- clude both quantitative (longer child care attendance) as well as qualitative inputs in educa- tion (teaching practices and class size), this dissertation acknowledges the complexity of educational policy.

A second, related contribution is the focus on the first half of childhood, i.e. the first ten years of a child's life. This focus is necessary, as general cognition has been found to be

1.3 Common Contributions

43 particularly malleable at this age (Cunha and Heckman, 2007). While crystallized intelli- gence may be acquired later on, fluid intelligence is rather stable after the age of ten (Hop- kins and Bracht, 1975). This means that any intervention aimed at increasing fluid intelli- gence is not only less efficient later on in the life cycle (as is the case with non-cognitive skills), but often outright ineffective.

The third common contribution is the fact that all chapters use data from Germany. This in itself is an advantage, as the effects of different interventions can be compared to each other and therefore provide decision-makers with a more comprehensive picture of their policy options. Furthermore, the general education system as well as the system of universal child care boast a number of specificities that complement the many studies in the field that originate from either the USA, the United Kingdom, or Scandinavia. Most promi- nent among these specificities is the fact that child care is heavily subsidized and therefore inexpensive as well as the fact that the school system is heavily tracked starting in second- ary school. The latter feature is especially relevant for long-run studies such as conducted in Chapter 2.

A fourth common contribution is of methodological nature and pertains to the attempt at identifying causal effects. Being able to establish causality between a reform (a treat- ment) and an outcome is crucial for policy purposes, as it gives decision-makers the maxi- mum amount of information on what to expect from their actions. The "gold standard" to- wards reaching this goal is to conduct carefully planned experiments (randomized con- trolled trials). By randomly assigning the treatment to a subgroup of individuals out of all participants in the experiment, it is possible to compare outcomes between the two groups that should not be different in any characteristic except their treatment status. This way, the problem of the missing counterfactual, i.e. that one and the same person cannot be observed both as treated and as untreated, is solved. However, in reality it is often not possible to conduct such experiments. This may have to do with practical reasons (e.g. lack of funding) or ethical reasons (see e.g. Athey and Imbens, 2017). Therefore, researchers routinely resort to natural (or quasi-) experiments (for an overview of often-employed quasi-experimental strategies, see Angrist and Pischke, 2009). Such quasi-experiments are characterized by the fact that, while not intended as experiments, treatment is still randomly assigned due to

Chapter 1 Introduction

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some specific feature of the reform or the setting in which the analysis takes place. In Chap- ter 2, this feature is the place of residence of the child's family. Since child care supply was higher in some places as opposed to others for arguably exogenous reasons, children living in areas with higher child care supply had a higher likelihood of entering child care early than others. This mechanism is exploited for identification in an instrumental variables framework(IV). In Chapter 3, I use fixed effects and correlated random effects estimation in a within-student between-subjects framework for identification. Instead of comparing treat- ed and untreated individuals, I am here comparing the same student's performance in differ- ent subjects and relate it to teachers' instructional practices. Based on some assumptions, this approach tries to mimic the (unattainable) ideal of observing the same individual in dif- ferent treatment statuses at the same time. Finally, in Chapter 4 another IV approach is em- ployed. This time, the size of the cohort a child was born into serves as the exogenous fea- ture that influences the likelihood of ending up in a larger or in a smaller class in primary school.

The fifth common contribution is the combination of different data sources that com- plement each other in the same study. As a rule, researchers try to exploit the kind of data that are most suited to answering the research question at hand. However, oftentimes no ideal dataset is available that caters to all empirical needs. For example, while administra- tive data often contain huge numbers of observations, they frequently suffer from limited background information on each individual. Furthermore, they usually do not contain sub- jective information on individuals, for instance on their future aspirations. On the contrary, survey data often provide rich sets of control variables but have the drawback of limited sample sizes. By merging different data sources or performing analyses on different datasets it is often possible to get the best out of different worlds. In Chapter 2, I merge administra- tive data on child care supply at the county level to survey data from the SOEP. In Chapter 3, data from two different surveys are merged, namely the 2011 waves of the TIMSS and PIRLS studies. In Chapter 4, administrative data on school enrollment is merged to an ex- traordinarily rich dataset of test scores for the full population of third-graders in the German state of Saarland. What is more, data from the German National Educational Panel Study (NEPS) as well as administrative data on enrollment and grade retentions in the state of

1.3 Common Contributions

45 Saxony are used to verify some of the predictions of the theoretical model that would not have been possible with the test score dataset from Saarland.

Finally, a sixth common contribution of all studies is their strong emphasis on effect heterogeneities. These heterogeneities can pertain to differential effects (or effect sizes) on different subgroups of the population or non-linear effects along the distribution of the main explanatory variable. As for population subgroups, all chapters separately estimate effects on boys and girls as well as students from different socio-economic backgrounds. Since there is no universally agreed indicator for socio-economic background, different measures that are frequently encountered in the literature are employed in different chapters. In Chap- ters 2 and 3, socio-economic background is determined by the level of education of the mother and both parents, respectively, while in Chapter 4 the number of books at home is used. Similarly, there are different ways of uncovering effect non-linearities. In Chapter 2, treatment dummies that split the linear treatment indicator on school grades into different segments are considered. In Chapter 3, a squared term of the treatment variable is added to the models, whereas in Chapter 4 spline regressions are carried out.

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