This paper utilizes parts of the data set used by Krueger (999), more precisely an identifier, a student and teacher background dataset, which were merged into one big dataset. As the project ran over four years and observed the same participating students over this period, the data is presented in a panel format. However, due to the dropouts of pupils and entries of new students during the observation period, it is an unbalanced panel. Fluctuations in the student body of the participating schools create certain limitations and challenges regarding the process of random distribution which led to a large number of missing values as some students were randomized in kindergarten, while others in first grade. Nevertheless, a drop of the missing values would limit the overall effect, so they are included in the following estimations. Krueger (999) underscores that it should be remembered that kindergarten attendance was optional in Tennessee, which results in a rising number of new STAR participants in first grade. In total, the dataset contains ,598 observations, i.e. pupils and originally 34 variables. Table shows the means of pupils characteristics by treatment status, i.e. assignment to a small, regular or regular aide class in kindergarten and first grade for a students first year of participation in the project. An observation of only new STAR participants accounts for the disproportional addition of new students into the experiment over time and thus, will be used in some of the following estimations to control for randomness following Krueger (999). The means are consistent with Kruegers (999) results. In the case of free lunch, the White/Asian, attrition rate and actual class size are almost identical. Here, receiving free lunch is a dummy indicator for the socio-economic status of the pupils family, thus their parents income, while White/Asian
refers to the racial mix in the classes. Angrist and Pischke (2009) point out that the attrition rate relates to the share of pupils leaving the project at one point before finishing third grade. It should generally be noted that kindergarten outcomes are more credible than first-grade outcomes as they do not include the attrition rate problem and are mainly unaffected by the randomization problem. The characteristics of a pupils age in 985 and their average test score in the underlying grade show some differences in comparison to Krueger (999). This can be explained by the incomplete data used in this paper and the different composition of variables. While Krueger (999) calculates the age of the students by using values quarterly, this paper calculates them on a yearly basis. Further, the average achieved test scores show slight discrepancies because Krueger (999) uses the average of three subjects of the Stanford Achievement Test (SAT), namely maths, reading and word recognition, while this paper takes the average of the maths and reading SAT only.
Figure illustrates the distribution of the pupils average percentile scores for kindergarten and first grade in a kernel density graph for the treatment statuses small and regular without a teaching aide. It is striking that students in smaller-sized classes achieve on average higher test scores in both grades. Beyond that, a comparison of the two grades shows that pupils who joined project STAR in kindergarten generally perform better than the ones joining in the following grade. As a reason for this, Krueger (999) proposes that there is a high chance of better-performing pupils going to kindergarten in contrast to weaker students traced back to the non-compulsory kindergarten attendance in Texas at the time, similar to weaker-performing students unproportionally added to the sample at a later point.