# **2.5 Goal-Setting**

Students in both treatment arms set goals in the form of a target score to achieve (out of 20 points) at the endline test. Figure 2 and 3 show the distribution of goals set (out of 20) for both treatment arms. As observed, the majority of students set very high goals. The distribution of set goals is remarkably similar across both the treatment arms, thereby providing evidence against any strategic goal-setting across arms. In an attempt to understand the goal-setting in detail, we plot the distribution of the gap between the set goal and actual baseline score for both the treatment arms in Figures 4 and 5 respectively. As observed, a majority of students have set very high goals in comparison to the actual baseline performance, and this pattern is similar across both the treatment arms. Most students have aimed at covering a gap of more than 10 points from their baseline score; a gap which is _more than half_ of the total points on the exam. Since the goals were set with a reference point of _expected score at baseline test_ , this large gap could be a result of the students’ _overestimating_ their baseline performance, the students being _overambitious_ about their future performance or both. We decompose the gap between the goal and the actual baseline score as follows: Where the first term on RHS is _overestimation_ and second term is _overambition_ . We classify a student in any treatment group as _overestimating_ their baseline performance if the gap between their expected and actual performance is equal to or more than half of the total points on test i.e. _≥_ 10 points. Similarly, a student setting a goal which aims at covering a gap of more than or equal to 10 points from their expected baseline score is termed _overambitious_ . Figure 6 and 7 show the distribution of overestimation for both treatment arms, and as observed, the majority of students overestimate their baseline performance by a factor of more than 10 points. Table A-1 shows the proportion of treated students in each of these categories. As observed, 60% of treated students are overestimating their baseline performance while only 6% set overambitious goals.

# **2.6 Outcome Measures**

We analyze the impact of the intervention on _six_ key outcome measures. We discuss the construction of these outcome measures below in detail: _Student Time-Use_ : In both the baseline and endline survey, we collected data on time use on an average weekday on various time use categories. These categories include: _studying and doing homework outside school_ , _helping family with household or other type of work_ , _sleeping_ , _playing games, chatting with friends etc outside school_ , _Studying extra for the endline exam_ , and _hours studying math outside school_ . Responses to these questions in survey are coded on an increasing scale of 1 to 5 with 1 being the lowest and 5 being the highest value.<sup>7</sup> Standardized values of responses to all these questions are converted to a single Anderson’s Index (see Anderson (2008)), called _Time-use Index_ .<sup>8</sup> _Effort Index_ : In the endline survey, we collected data on measures of effort students have put in the class and for exams using questions related to their studying habits in the class and for exams. These questions are Likert scale responses to statements like _I studied regularly_ , _I tried to do well compared to other students_ , _I tried to get a better score than the last year_ , _I actively participate in class discussions_ , _I_

> 7Responses range from _Usually not at all_ coded as 1 to _More than X hours_ coded as 5.

> 8Responses for _Sleeping_ , _Helping with family work_ , _Sleeping_ and _Playing games etc._ are reverse coded as these are likely the substitutes for spending more time in studying.

6