metadata: authors: 0: author: Islam,Asad
1: author: Kwon,Sungoh
2: author: Masood,Eema
3: author: Prakash,Nishith
4: author: Sabarwal,Shwetlena
5: author: Saraswat,Deepak
count: Tanzania
docna: 0: docna: When Goal-Setting Forges Ahead but Stops Short
docty: Policy Research Working Paper
subtopic: Educational Sciences,Gender and Development,Educational Institutions & Facilities,Effective Schools and Teachers,Financial Sector Policy
teratopic: Education,Gender
abstracts: cdata!: This paper reports the results of an
            at-scale randomized controlled trial among 18,000 secondary
            students in Zanzibar (Tanzania) to examine the effects of
            personal best goal-setting on student outcomes. The paper
            also tests the impact of combining goal setting with
            non-financial rewards conditional on students meeting the
            goals they set. The results suggest that goal-setting has a
            significant, positive impact on students' time use,
            study effort, and self-discipline. However, there are no
            significant impacts on test scores. This is partially
            because nearly two-thirds of the students do not set
            realistic goals. The paper finds that the effects on time
            use, study effort, and discipline are weaker when goal
            setting is combined with nonfinancial rewards. This suggests
            that tying goal setting to extrinsic incentives could weaken
            its impact. The results show stronger impacts for female
            students and from students from weaker socioeconomic
            backgrounds. These results demonstrate that goal setting can
            have positive impacts on student outcomes, especially for
            the relatively disadvantaged. However, for maximizing the
            impacts, goal setting may need to be combined with guidance
            on setting realistic goals, and extrinsic rewards tied to
            goals may need to be avoided.
geo_reg_key: 517191
repnme: repnme: When Goal-Setting Forges Ahead but Stops Short
display_title: When Goal-Setting Forges Ahead but
            Stops Short
listing_relative_url: /research/2020/09/32421629/goal-setting-forges-ahead-stops-short
topicv3: Teachers Management m1326119 1523,Education m1327048 470,Gender m1327621 615,Financial Sector Development m1327681 1920,Psychiatry, Psychology m1504686 3613
publishtoextweb_dt: 2020-09-21T00:00:00Z
historic_topic: Education,Gender
origu: Education SAR (HSAED)
no_of_pages: 41
author_keys: 0: author_key: 1402074
1: author_key: 1151157
2: author_key: 950739
3: author_key: 1796720
model_extractions: Policy Research Working Paper 9406 When Goal-Setting Forges Ahead but Stops Short _Asad Islam Sungoh Kwon Eema Masood Nishith Prakash Shwetlena Sabarwal Deepak Saraswat_ Education Global Practice September 2020
Policy Research Working Paper 9406

# **Abstract**

This paper reports the results of an at-scale randomized controlled trial among 18,000 secondary students in Zanzibar (Tanzania) to examine the effects of personal best goal-setting on student outcomes. The paper also tests the impact of combining goal setting with non-financial rewards conditional on students meeting the goals they set. The results suggest that goal-setting has a significant, positive impact on students’ time use, study effort, and self-discipline. However, there are no significant impacts on test scores. This is partially because nearly two-thirds of the students do not set realistic goals. The paper finds that the effects on time use, study effort, and discipline are weaker when goal setting is combined with nonfinancial rewards. This suggests that tying goal setting to extrinsic incentives could weaken its impact. The results show stronger impacts for female students and from students from weaker socioeconomic backgrounds. These results demonstrate that goal setting can have positive impacts on student outcomes, especially for the relatively disadvantaged. However, for maximizing the impacts, goal setting may need to be combined with guidance on setting realistic goals, and extrinsic rewards tied to goals may need to be avoided.

This paper is a product of the Education Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ssabarwal@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# When Goal-Setting Forges Ahead but Stops Short<sup>_∗_</sup>

Asad Islam<sup>_†_</sup> Sungoh Kwon<sup>_‡_</sup> Eema Masood<sup>§</sup> Nishith Prakash<sup>_¶_</sup> Shwetlena Sabarwal<sup>_∥_</sup> Deepak Saraswat<sup>_∗∗_</sup>

## _JEL Codes_ : D9, I20, I25, O15, O55. Keywords: Goal-Setting, Recognition Rewards, Student Performance, and Zanzibar.

> _∗_ We appreciate helpful comments and suggestions from Nathan Fiala, Caroline Hoxby, Tarun Jain, Matt Lowe, Michael Manove, Nirajana Mishra, Dilip Mookherjee, Priya Mukherjee, Abhiroop Mukhopadhyay, Philip Oreopoulos, Daniele Paserman, Gautam Rao, Michael Kremer, Stephen Ross, Frank Schilbach, Samer Al Samarrai, Inaam Ul Haq and seminar and conference participants at Boston University, University of Connecticut, and 14th Annual Conference on Economic Growth and Development, Norms and Behavioral Change Conference (2019), and 17th Midwest International Economic Development Conference. This work was made possible through the leadership and support of Abdulla Mzee Abdulla and Khalid Wazir from the Ministry of Education and Vocation Training (MoEVT) in Zanzibar. We are also grateful for the support of Huma Ali Waheed and Kaboko Nkahinga from the World Bank. Tumainiel Ngowi provided excellent field support. Funding from the Results in Education for All Children (REACH) Trust Fund is gratefully acknowledged. Declarations of Interest: None.

> _†_ Monash University. `asadul.islam@monash.edu` .

- Korea Institute of Public Finance. `sokwon@kipf.re.kr` .

- §World Bank. `emasood@worldbank.org` .

- University of Connecticut, IZA, HiCN, GLO, and CReAM. `nishith.prakash@uconn.edu` .

> _∥_ World Bank. `ssabarwal@worldbank.org` .

_∗∗_ University of Connecticut. `deepak.saraswat@uconn.edu` .
# **1 Introduction**

Despite making tremendous progress in improving school enrollment, many countries struggle with a low level of student learning. For example, in India, among children in grade 8, only 22.1% can do subtraction, and 43.9% can divide (ASER (2017)).<sup>1</sup> The low level of student learning has longrun implications on their lives and represents lost output for the economy as a whole (Michelmore & Dynarski (2017); Hanushek (2009)). Policies aiming to improve academic performance have extensively focused on using financial incentives, such as paying students for their improved test scores.<sup>2</sup> However, studies using both randomized and natural experiments on the impact of financial incentives to improve student performance have been mixed and inconclusive.<sup>3</sup> In addition to being expensive and difficult to scale up, evidence from the psychology literature argues that financial incentives might crowd out intrinsic motivation to study (e.g., Cameron & Pierce (1994), and Gneezy _et al._ (2011)). On the other hand, self-selected goals and non-financial incentives, given their self-selected, nature may tie well with intrinsic motivation. In this paper, using a large, randomized controlled trial, we test the impact of _goal-setting_ and a _non-financial incentive_ (recognition award) on _student effort_ and _test scores_ in Zanzibar.

There is a strong belief in the psychology literature that goals can act as powerful motivators that may affect both thought and action towards improving an outcome (Locke (1968); Locke & Latham (1990); Heath _et al._ (1999)). The foundation of this dates back to the prospect theory that suggests that goals can act as reference points (Kahneman & Tversky (1979)), with the psychological motive of loss aversion causing individuals to want to reach their goals (Heath _et al._ (1999)). Also, goals act as a self-imposed commitment device that is used as a tool to motivate oneself, increase effort, persistence, discipline, self-regulation, etc. Therefore, in the context of education, goal-setting could act as a motivation for students to increase effort to achieve those goals (see Church _et al._ (2001), Locke & Latham (1990), Wiese & Freund (2011)), thereby improving their academic performance.

In this experiment we primarily focus on personal best (PB) goal-setting (goals set by students themselves), as opposed to goals set by others (e.g., teachers, parents, or counselor). PB goal-setting is a goal-setting approach that can be personalized to each student’s degree of self-control, therefore encouraging students to focus on personal improvement and striving to outperform their best past personal efforts, rather than the efforts of others or achieving against the absolute criteria of the task (Martin (2006); Elliot (1999); Pintrich (2000); Martin & Elliot (2016a)). This approach has been suggested as one way of optimizing students’ academic performance (Martin (2006); Martin (2011)). According to the goal-setting literature, specific and challenging goals lead to better performance since these goals reduce the ambiguity of what is to be achieved (Locke & Latham (2002)). In addition, PB goal-setting is associated positively with growth mindsets, achievement, engagement, and academic outcomes (Burns _et al._ (2017); Martin & Liem (2010); Martin & Elliot (2016b); Martin & Elliot (2016a)). The underlying theory is that by when students choose their own goal it “acts as an internal commitment device meant to overcome problems of self-control” (Royer _et al._ (2015); Samek (2016)).<sup>4</sup> In addition to intrinsic motivation (e.g. goal-setting in our context), economic theory suggests that

> 1Similarly, among children in grade VIII, only 13.2% can read grade I level text but not grade II level text, and 72.8% can read grade II level text.

> 2In Fryer (2011), in one of the interventions they paid second graders $2 per book to read and pass a short quiz to confirm they read it.

> 3Studies estimating the impact of financial incentives on college students performance have yielded mixed results: Henry _et al._ (2004), Cha & Patel (2010), Scott-Clayton (2011), De Paola _et al._ (2012) and Castleman _et al._ (2014) report positive effects; while Cornwell _et al._ (2005), Angrist _et al._ (2009), Leuven _et al._ (2010), Patel & Rudd (2012) and Cohodes & Goodman (2014) do not find any significant positive effects.

> 4Goal-setting theory is a key conceptual underpinning of PB goal-setting (Locke & Latham (1990)). According to Martin (2006), Martin (2011), PB goals are closely linked to goal-setting capacity Locke & Latham (2002).

1
extrinsic motivations can act as “status incentives” for an agent to increase effort and achieve better outcomes (see Besley & Ghatak (2008)). Ashraf _et al._ (2014) in an experimental study in Zambia finds that employer recognition increases effort and performance. Students, when faced with extrinsic incentives and gains in the form of recognition conditional on an increase in performance, may increase study effort. A combination of an extrinsic and intrinsic incentive (PB goal) may have higher gains than PB goal-setting alone. On the other hand, there is evidence that in cases where the effort is put towards tasks which are _moral_ or _social_ in nature, the extrinsic incentives may crowd out intrinsic motivations (See Bowles (2008) and Heyman & Ariely (2004)).

Improving the academic performance of students using _cost-effective_ and _scalable_ incentives has been a challenging endeavor and a focus of researchers studying education. Therefore, from a policy standpoint, PB goal-setting offers a low-cost, scalable option with intrinsic merit beyond its instrumental value in promoting student achievement. In this paper, using a large _at-scale_ randomized controlled trial in _all_ secondary schools in Zanzibar (187 schools with 18,281 students in grade 8), we answer two important questions in the field of behavioral development economics. First, do self-set goals provide sufficient impetus for improved student effort and academic performance measured by test scores? Can their efficacy be improved through extrinsic incentives (recognition award) tied directly to goal achievement?

We answer these two questions by randomly assigning the 187 secondary schools into one of the two treatments and one control group. In the first treatment, we encourage students to set their own personal best (PB) goals for improvements in math test scores. In the second treatment, we combine the goal-setting in the first treatment with performance-based non-financial recognition awards (medals, certificates, backpack, etc.) for achieving the self-set goals. Locke (1968) argues that such incentives affect performance only through their effect on goals. Also, such non-financial recognition awards lead to several non-material benefits, which come in the form of social-recognition from teachers, peers, or society. This recognition is related to the status of the winner of a non-financial award within the group (e.g., the classroom, the school, or society). We test whether these two treatments lead to improved education outcomes of students in secondary schools.

We find that self-set personal goals lead to a significant _positive_ impact on self-reported time use, student effort, and self-discipline. However, we find that self-set personal goals _do not_ have discernible impacts on student’s test scores in the short run. While test scores show a positive and small improvement, it is not statistically different from zero. We also look at a measure of student confidence and find that the intervention _did not_ have any statistically significant impact. Looking at the treatment effects separately by gender, we find that female students improve on all the main outcomes much more than the male students. Combining the two treatments, i.e., goal-setting with performance-based non-financial recognition award shows similar trends but weaker as compared to the pure goal-setting treatment. We discuss these results in light of the literature on extrinsic versus intrinsic incentives. We explore various heterogeneities by students’ socio-economic backgrounds since they are likely to respond differently to the intervention. We explore this by looking at students with diverse English language skills of parents and by the level of household wealth. Results suggest that students having parents who cannot read and write English improve more on time use, effort, and self-imposed discipline as compared to students having parents who can read and write English. We observe similar effects when we look at students coming from families with wealth levels below the median of the entire study sample. In particular, students coming from lower wealth levels show larger improvements in time use, effort, and self-imposed discipline as compared to their richer counterparts. In addition to these heterogeneities, we use the distribution and characteristics of the goals set by students to analyze how the impact varies by students’ perceptions of their ability.

This paper contributes to several related literatures. Most narrowly, our findings contribute to 2
the literature using experiments to estimate the impacts of self-set personal best goals on academic performance in various settings. Recent experimental studies in the United States and Canada use a variety of goal-setting interventions and incentives related to academic performance and find mixed results on academic outputs.<sup>5</sup> Among the closest to our study, Clark _et al._ (2017) in the context of undergraduate students in a public university in the United States find that only the goals which are specific to certain academic tasks show improvements in completion and performance. On the other hand, in a developing country context, Mukherjee & Poonuganti (2019) find no overall impact of parents’ involvement in setting goals and aspirations on their children’s academic outcomes in India. Dobriyoni _et al._ (2017) in the context of college education in Canada find no impact of goal-setting exercises on GPA, course credits, or persistence in subsequent years of education. Lent (2018) using a similar setting finds no impact of goal-setting on undergraduate academic performance and attributes this to the rigidity of set goals. Another related experiment by van Lent & Souverijin (2017) analyzes the effects of setting a goal and increasing its ambitiousness using mentor-student meetings involving first-year university students and finds students in the treatment groups performed better, however, students who were challenged to set a higher goal performed significantly worse than comparable students in the goal treatment. Our paper adds to this growing branch of literature around goalsetting in an academic context where we find evidence that while the outcomes like effort, time-use, and discipline move in the right direction, but for these to translate into improved test scores, we would probably need much larger movements in these behavioral measures to start with.

Additionally, an important contribution of this paper is to extend the goal-setting literature to the context of a developing country and pre-college (secondary school) setting. The targeted student population is of particular interest to the policymakers given very high rates of student drop-out around this age. In Zanzibar, almost half of the students entering secondary schools drop out before the completion. Also, the transition from lower secondary to higher secondary is only 8.4 percent (MOEVT (2017)). Evidence suggests that most students drop-out due to poor performance in lower secondary exit examinations.

We make a modest contribution to the very few empirical papers that have analyzed the role of ‘status’ and ‘social recognition’ in the context of economics (Ball _et al._ (2001); Markham _et al._ (2002); Charness _et al._ (2010) and Kosfeld & Neckermann (2010)). Although in this paper we do not directly test the pure ‘status’ dimension of awards and student recognition, we estimate if such awards compliment _(or not)_ the impact of PB goal-setting on students’ academic performance, especially if tied directly to goal achievement.

Finally, to the best of our knowledge, this is the first paper conducting an _at-scale_ randomized experiment related to goal-setting. While in theory smaller-scale experiments can test and inform a potentially large-scale program rollout, due to governmental and bureaucratic constraints, it does not happen as often. An intervention as cost-effective as goal-setting is easier to roll out at a larger scale and hence is better tested at such a large scale. In addition to that, large-scale experiments not only circumvent the problem of external validity in a randomized experiment, but also avoid the issue of program effects being different at a smaller scale versus at a larger scale (Muralidharan & Niehaus (2017)).

> 5See Clark _et al._ (2017), Lent (2018), Morisano _et al._ (2010), Levitt _et al._ (2016), and O’Neil _et al._ (1995).

3
# **2 Experimental Design**

## **2.1 Context**

Zanzibar, off the coast of mainland Tanzania, is a semi-autonomous archipelago, that comprises of two main islands: Unguja and Pemba, and multiple smaller islands around the region. The Government of Zanzibar acts independently from Tanzania on all matters other than foreign policy. Zanzibar’s economy is mainly supported by the service industry, with tourism contributing to 51% of the GDP (Mosedale (2010)). The total population of Zanzibar is estimated to be around 1.6 million in 2015 (OCGS (2016)), with around two-thirds living in Unguja. The literacy rate, as defined by the percentage of people above 10 years of age who can read and write simple statements was around 84% in 2016 (MOEVT (2017)). This figure was slightly lower for females at around 79%. Compared to Tanzania as a whole, the literacy rate is 5-10 percentage points higher in Zanzibar (MOEVT (2017)).

Education is considered a basic human right in Zanzibar, and is free at the primary level. The education structure is organized as two years of pre-primary, then six years of Primary schooling starting at six years old. From here students move on to Lower Secondary for a total of four years before starting Advanced secondary school for an additional two years. Once they clear Advanced Secondary, they can move on to Higher Education. The language of instruction is English from Standard 5 onwards, henceforth, all subjects, except Kiswahili, are taught and tested in English.

Student performance in national exams is generally poor. Around one-fifth of all students taking the secondary school entrance exam failed to pass. Students’ performance in Mathematics was observed to be especially low. At the lower secondary level, only around half of all students managed to pass the Form 2 exam (lower secondary level or grades 8 and 9), while the rest comprised those who failed or did not take the exam. High levels of variation are found across the subjects in the Form 2 exam, with students scoring around a 45% in the Kiswahili exam on average, while only managing a 15% average in Math. Dropout rates are especially problematic at the ordinary secondary level, with around 30% of the students failing to pass the Form 2 exam, and around half of all students leaving the system before the end of the four-year cycle (MOEVT (2017)).

## **2.2 Intervention and Timeline**

We conduct the nationwide experiment in Zanzibar where all grade eight students in public secondary schools were a part of the study sample. There were a total of 187 secondary schools randomly assigned to two treatments and one control group (see Table 1 for sample sizes). Goals in both treatment arms were set following Martin & Elliot (2016b) and Martin & Elliot (2016a). Treatment announcement was preceded by a baseline data collection and baseline math and English test. After the treatment was announced and before the endline data collection and test, students were reminded of their goals. Table 2 shows the timeline of the study, interventions, and reminders.

The _Treatment 1_ group, also known as “goal-setting” received the personal best goal-setting intervention. In this group, the enumerators introduced the concept of a Personal Best goal to the Form 2 students, using a given script (see Figure 8). The enumerators then used an interactive exercise to ensure students understood the meaning behind a personal best goal. Before we asked students to set their goals, we conducted a standardized baseline test for students in English and Mathematics using a curriculum-based assessment specifically developed for the study. Students were asked to set their personal goals soon after the baseline test was completed, and based on their expected score in the baseline test. This was a personal best goal for themselves for a similar exam at the end of the year (about 9 months). Students were asked to think about these goals carefully, allowing for improvement while keeping them realistic.

4
The _Treatment 2_ group, also known as “goal-setting + Recognition” received the personal best goal-setting intervention as in Treatment 1, but their ability to meet their personal best goals was tied to a Non-Financial recognition reward. These rewards were in the form of certificates of achievement given in a ceremony in front of the whole school. Students were made aware of this reward as part of the given script in Treatment 2 schools (see Figure 10).

After the treatment announcements, teachers and head-teachers in the two treatment groups were asked to give students periodic reminders of the goals they had set. Schools also received a poster to display in the school, reminding students about working on their goals every month. Systematic field-based reminders of the Personal Best goal-setting intervention was also undertaken in the two treatment schools. Each student was individually shown the goals they had set for themselves earlier that year as a reminder. Finally students were told that the endline exam would be undertaken at the end of that year, to encourage them to work on their goals.

# **2.3 Data Collection**

Baseline data collection was conducted in February 2016, which included: (i) Survey with the Head Teacher, (ii) Survey for the Form 2 English and Math teachers, and (iii) Form 2 Student Survey and Assessment. At the end of the data collection, the enumerators were instructed to make announcements to the 2 treatment groups on goal-setting exercises, and students in the treatment groups were given a (iv) Treatment Sheet to record their goals. The baseline student sample consists of around 18,281 students from all schools. Endline data collection was conducted in mid to end October 2016, which included: (i) Survey with the Head Teacher, (ii) Survey for the Form 2 English and Math teachers, and (iii) Form 2 Student Survey and Assessment in English and Math. Only students from the baseline were tested in the endline.

# **2.4 Validity of the Experimental Design**

To ensure that the randomization was successful and treatment and control schools were similar before the experiment, we perform a balance test on student and school characteristics respectively in Tables 3 and 4. We do not find any statistically significant difference in school-level characteristics, key demographic characteristics of students, and baseline achievement across treatment and control groups. Most importantly, there are no statistically significant differences across the treatment and control students on age, financial status, and baseline test score.<sup>6</sup> However, there are small imbalances on student gender. About 26% of students were absent during the endline data collection which gives us 13,426 students on which the final analysis is conducted. Table 3 shows that this attrition rate was not statistically different across study groups. After presenting the main results, we will revisit this issue of attrition and attempt to understand and alleviate concerns around its potential impact on the results.

The average age of students in the study is about 16 years and 7 months. Around 55 percent of the students are female; 74 percent reported living with both parents; 6.4 percent are repeating their current grade, and 9.7 percent are new to their respective schools. On average, students reported spending 3 hours a week studying for Mathematics outside of their school, and around 47 percent reported attending exam preparation classes for Mathematics.

> 6Figure 1 shows a similar distribution of test scores in mathematics across groups at the baseline.

5
# **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
_prepare and review lessons_ , and _I plan and organize my school work_ . These statements were ranked by students on a Likert scale of 1 (strongly disagree) to 4 (strongly agree). We combine the standardized values of these responses to form a single Andersons’s Index called _Effort Index_ .

_Self-Discipline Index_ : We collected student’s responses to statements measuring the degree of selfdiscipline in a student’s life. These statements are: _I like to be very good at what I do_ , _I can be very disciplined and push myself_ , and _I finish whatever I begin_ . These statements were ranked by students on a Likert scale of 1 (strongly disagree) to 4 (strongly agree). We combine the standardized values of these responses to form a single Andersons’s Index called _Self-Discipline Index_ .

_Confidence Index_ : We collected the student’s response to statements measuring the level of confidence. These statements are _I feel very confident in exam_ , _I feel very confident when I play with my friends_ , and _I feel very confident talking to my teachers and responding to their questions in class_ . These statements were ranked by students on a Likert scale of 1 (strongly disagree) to 4 (strongly agree). We combine the standardized values of these responses to form a single Andersons’s Index called _Confidence Index_ .

_Aspirations Index_ : We collected student’s responses to statements measuring the level of aspirations. The statements are: _I have high goals and aspirations_ , _I do not expect much from my future_ , and _I have a desire to pursue further education_ .<sup>9</sup> Their statements were ranked by the student on a Likert scale of 1 (strongly disagree) to 4 (strongly agree). We then combine the standardized values of these responses to form a single Anderson’s Index called _Aspirations Index_ .

_Test Score_ : The goal-setting exercise in both the treatment arms were in connection with the Math test scores. We administered a Math test at baseline followed by the same test (with questions ordered differently) at the endline. We use these endline test scores as our outcome of interest. We standardize the raw scores by creating z-scores for both endline and baseline scores.<sup>10</sup> We also report similar z-scores for the English test which were administered during baseline and endline.

_Parent’s and Teacher’s Efforts Index_ : In the endline survey we ask students questions related to teacher and parent’s effort and we combine them to form indices for teacher and parent’s effort.<sup>11</sup> Questions related to teacher’s effort are: _Did your teacher assign any homework in last week?_ , _Did your teacher give quizzes or tests in last month?_ , and _If you had questions or problems, could you discuss with your teacher freely?_ Questions related to parent’s effort are _During the last week, have your parents asked about your school life?_ , _During the last week, have you worked on school work with your parents?_ , and _During the last week, have your parents checked if you did the homework?_ We combine the standardized values of these responses to form two Anderson’s Index called _Teacher Effort Index_ and _Parent Effort Index_ .

# **2.7 Estimating Equation**

We are interested in estimating the impact of PB goal-setting (Treatment 1: GS) and PB goal-setting with public recognition (Treatment 2: GS + R) on outcomes of interest. We estimate the following equations to evaluate the impact of the two treatments:

> 9The statement _I do not expect much from my future_ was reverse coded.

> 10We use the control group as the base category. The formula used is:<sup><u>(</u></sup> Standard<sup>RawScore</sup> Deviation<sup>-Meanof</sup> of<sup>Control</sup> Control<sup>Raw</sup> Raw<sup>Score</sup> Score<sup><u>)</u>.</sup>

> 11The responses to these questions are recorded in Yes or No.

7
where, _i_ is the student in school _S_ . _Yis_<sup>_Post_</sup> is the outcome of interest observed at the endline. _Ts_<sup>_GS_</sup> and _Ts_<sup>(</sup><sup>_GS_+</sup><sup>_R_)</sup> denotes goal-setting and goal-setting + recognition treatments respectively. _Yis_<sup>_Pre_</sup> is the baseline value of outcome observed at the endline. _β_ 1, and _β_ 2 are our main coefficients of interest and provides the _intent-to-treat_ estimate, which is the effect of goal-setting and goal-setting + public recognition on the outcomes of interest. We also estimate a modified version of equation 1 for the pooled treatments ( _Ts_<sup>_GS_</sup> + _Ts_<sup>(</sup><sup>_GS_+</sup><sup>_R_)</sup> ). _εis_ is the error term. We cluster the standard errors at the school level since randomization is at the school level.

# **3 Results**

## **3.1 Average Treatment Effects**

We first present the estimates of the impact of goal-setting and goal-setting combined with recognition on our first-stage outcomes: _Time-use Index_ , _Effort Index_ , _Self-Discipline Index_ , _Confidence Index_ , and _Aspirations Index_ in columns 1-5 in Table 5. These are important behavioral changes that have been shown in the literature to be highly predictive of educational outcomes (Heckman _et al._ (2006); Almlund _et al._ (2011); Alan _et al._ (2019)). We present the same for the pooled treatment in Table A- 2. We further break down the aggregate indices reported in Table 5 into their individual components and present the estimates in Table 6 to Table 10. This allows us to examine the variables driving the observed effects in the aggregate index for _Time-use_ , _Effort_ , _Self-Discipline_ , _Confidence_ , and _Aspirations_ .

We present the estimates of the impact of the two treatment arms for the _Time-Use Index_ in column 1 of Table 5 and find that both treatments led to a significant change in student’s time use behavior. The effect ranges from 11.3% of a standard deviation (s.d) due to goal-setting alone to 10% of a standard deviation due to goal-setting plus the recognition award. These estimates are statistically significant with a p-value of less than 0.01 and 0.05, respectively. Table A-2 shows the overall effect of goal-setting on the _Time-use Index_ by pooling both the treatments (in column 1). The estimates suggest an aggregate effect of 10.7% of an s.d with smaller standard errors. We then present the estimates for individual components of the _Time-use Index_ in Table 6 and find important behavioral changes. In particular, we find that the positive impact on the _Time-use Index_ is driven by a reduction in helping with household work (column 2 of panel A), reduction in sleeping time (column 3 of panel A), and most importantly through an increase in study time outside school for Math (column 3 of panel B).

In column 2 of table 5, we present the estimates for _Effort Index_ and find that while the goal-setting treatment is associated with a 10.6% of a s.d increment in effort in class and for exams, combining goal-setting with recognition generates a positive but smaller and statistically insignificant effect on the _Effort Index_ . Pooled results in Table A-2 shows a positive and statistically significant (with a p-value less than 0.1) aggregate effect of goal-setting on _Effort Index_ (column 2). We then present the estimates for individual components of the _Effort Index_ in Table 7 and find that the improvements in the _Effort Index_ is being driven by _effort to get better score than last year_ (column 3 of panel A), _active participation in class discussions_ (column 1 of panel B), and _planned and organized school work_ (column 3 of panel B).

In column 3 of Table 5, we present the estimates for _Self-Discipline Index_ and find that the goalsetting only shows a 9% of an s.d increment in discipline index, but while the effect of goal-setting and recognition is positive, it is smaller and statistically insignificant. Pooling both treatments together Table A-2 shows a positive aggregate effect of goal-setting exercise on the _Self-Discipline Index_ (column 3). Table 8 shows that all components of the index except the response to statement _“I can be very_ 8
_disciplined and push myself”_ show improvements.<sup>12</sup> In column 4 of Table 5, we analyze if goal-setting affected student’s personalities by looking at the impact of the two treatments on a measure of student confidence: _Confidence Index_ . We find that the impact of the goal-setting treatment on _Confidence Index_ is positive but very small and statistically insignificant. Similarly, goal-setting combined with recognition induces a negative but small effect that cannot be statistically distinguished from zero. Column 4 in Table A-2 shows that when both treatments are pooled, there is a null aggregate effect of goal-setting exercise on _Confidence Index_ .

In column 5 of Table 5, we analyze if goal-setting affected student’s aspirations. We find evidence that the goal-setting intervention had positive but very small and statistically insignificant on _Aspiration Index_ , while this is negative and statistically insignificant for goal-setting with recognition arm.<sup>13</sup> In Table A-2, we find that pooling both the treatments has a small negative impact which cannot be distinguished from zero. We find the results on aspirations consistent with the literature in psychology.<sup>14</sup> So far, we have shown that the goal-setting intervention has an impact on various behavioral outcomes, which directly feeds into the students’ education production function and _likely_ change test scores. We test the impact of the two treatment arms on the z-scores of endline Math test scores in column 5 of Table 5. We find that both the treatments led to a positive but small and statistically insignificant gain in test scores. We present the same for the pooled treatments in Column 5 of table A-2, and find that pooling lowers the standard errors but given the smaller effect size, it remains statistically insignificant. Improving test scores has not been trivial in the education literature and has mostly been concentrated in studies testing expensive interventions which, unlike behavioral interventions, have a direct impact on the cost of getting an education or on classroom instruction.<sup>15</sup> Only a handful of behavioral interventions have shown positive impact on test scores.<sup>16</sup> Our results are consistent with Oreopoulos & Petronijevic (2019) and Dobriyoni _et al._ (2017) who _do not_ find the impact of the social psychology interventions on academic performance in their studies in Canada.

Overall, our results suggest that while goal-setting induces behavioral changes in the right direction by increasing students’ time use for study, the effort for study, and discipline, these effects are probably not large enough to translate to improvements in test scores. Our results also indicate that goalsetting only improved factors having a direct connection to studies and not so much in improvements in personality, such as confidence.

The analysis in Table 5 is looking at five different outcomes for two treatments each (a total of 10 comparisons). Therefore, a conventional statistical significance observed in outcomes does not rule out the presence of “false positives” due to multiple hypothesis testing. We subject all these 10 comparisons to false discovery test as per Benjamin-Hochberg procedure (see Benjamini & Hochberg (1995)) and find that all results which show statistically significant movements pass the B-H test.<sup>17</sup>

> 12Statement “I can be very disciplined and push myself” shows positive impact but is small in magnitude and statistically insignificant.

> 13Table 10 shows that there is no statistically significant impact on components of aspiration.

> 14Literature in psychology demonstrates that aspirations are shaped early in a child’s life and tend to decline, become less flexible in response to growing understanding of the world (Gutman & Akerman (2008). Among studies that find changes in aspirations among students, it is often a long term intervention like participation in athletics (e.g. see Hwang _et al._ (2016)) that results in these changes.

> 15Muralidharan _et al._ (2019), Muralidharan & Sundararaman (2011) and Fiala _et al._ (2019) are examples of a few such studies.

> 16A few notable examples include Bettinger & Baker (2014) and Alan _et al._ (2019).

> 17 With a chosen false discovery rate of 0.1 and 0.2.

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# **3.2 Heterogeneities**

In this section we conduct two heterogeneity analyses around the main outcomes by gender, socioeconomic status of the households, and estimation of own ability.

## **3.2.1 Gender**

Male and female students might react differently to being in one of the two treatments. Recent studies testing interventions targeted at improving student outcomes either do not explore this possibility, or do not find differential impacts by gender.<sup>18</sup><sup>_,_19</sup> We analyze the treatment effects separately for each gender and report the results in Panels A and B of Table 11. Panel A suggests that while male students do show positive gains, these are very small and statistically insignificant, except for the time-use. On the other hand, female students in panel B show larger and statistically significant gains in time-use, effort, and self-discipline. They also show a larger magnitude of gain in test score but it is not statistically significant. These results are particularly important since it provides evidence that goal-setting treatment might have appealed more to female students than their male counterparts. We are not aware of any studies in a developing country context that shows the gender differences in such behavioral interventions.

## **3.2.2 Socio-economic Status**

Students belonging to different socio-economic status might demonstrate a varied level of motivation when subjected to the goal-setting treatment. Dobriyoni _et al._ (2017) finds some suggestive evidence that students with English as their mother tongue gained more from goal-setting in the context of college education.<sup>20</sup> Muralidharan _et al._ (2019) finds no differential impact by socio-economic status for an intervention which leads to a substantial change in test scores.

We analyze this by dividing the sample by: _Parents being able to read and write English_ and by _Household wealth_ . Panels A and B of Table 12 reports the results for students whose neither parents read/write English and for students whose either parents read/write English. As observed, while both sets of students show gains in time-use, effort, and self-discipline, students with non-English speaking/writing parents show much larger and statistically significant gains as compared to students with parents who can read/write in English. To look at richer versus poorer students, we divide the sample into higher or lower than the median of the asset index at the baseline. We present the estimates in Panels A and B of Table 13. Students from poorer households demonstrate larger and significant gains in time-use, effort, and self-discipline as compared to students from richer households.

Both the set of comparisons by socio-economic status demonstrate that students coming from comparatively disadvantaged backgrounds get larger gains from the goal-setting intervention. While our study is not equipped to delve deeper into the potential reasons, disadvantaged students are likely more motivated to improve or there is more room for improvement for students from the left tail of the distribution in academic performance.

## **3.2.3 Estimation of Own Ability**

In section 2.5 we analyze the distribution of goals set by treated students and find that majority of students have set very high goals, which in large part, is explained by students overestimating

> 18Dobriyoni _et al._ (2017) evaluate interventions related to goal-setting in the context of college education, but do not explore the effects differentially by gender of students.

> 19Muralidharan _et al._ (2019) do not find differential impact by gender of a tutoring intervention that shows substantial overall on test scores of students in urban India.

> 20These results, however, do not pass multiple hypothesis testing.

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their baseline performance. Using the definition of overestimation discussed in section 2.5, we find that students who overestimate their baseline performance scored lower in the baseline math test.<sup>21</sup> Contrary to the literature looking at gender differences in overconfidence, in our sample of treated students we find that girls overestimate their baseline performance more than boys.<sup>22</sup><sup>_,_23</sup> We analyze the treatment effects by dividing the treated sample into students _overestimating_ their baseline performance and students _not overestimating_ their baseline performance. We then compare these treated samples to the entire control group. We present the estimates in Table 14. We find that students who overestimate their baseline performance (Panel A) do much better in time-use, effort, and self-discipline as compared to students who do not overestimate their baseline performance (Panel B). We do observe some treatment effects on time-use and English scores in Panel B, although the English test was not part of the goal-setting exercise. Since the students who do not overestimate their baseline performance had higher baseline performance than their overestimating counterparts, it cannot be ruled out that the treated groups in Panel B are higher ability on average as compared to control, and the movements in coefficients are capturing underlying baseline differences.

# **4 Robustness**

## **4.1 Do Teachers and Parents Alter Their Behavior?**

A natural concern in a cluster-level randomization (schools in this study) is that teachers may alter their performance and effort to increase students’ performance and in that sense, the treatment effect we observe on certain outcomes may be the result of teachers altering their behavior in connection to the treatment and not of the goal-setting per se. The same concern also holds for parents altering their inputs in children’s study. Table A-3 estimates equation 1 with parents’ and teachers’s effort indices as outcomes and finds that both of them _do not_ demonstrate a value statistically different from zero. This analysis attenuates the concern that the observed treatment effects are a result of altered parents’ and teachers’ efforts.

## **4.2 Attrition**

As discussed in Section 3.1, we have substantial attrition in the study from baseline to endline. We have attrition ranges from 24.07% in (Goals + Recognition) the treatment to 28.25% in the control group. In this sub-section we aim to understand the attrits and if they can potentially induce any _upward bias_ in the observed treatment effects.

In Table A-4 we analyze the nature of attrits by looking at the association of attrition with baseline variables. As observed, girls are less likely to attrit compared to boys. Students who are repeating the grade or are new to the school are more likely to attrit. Looking at time-use and baseline Math test scores, it turns out that attrits had lower scores and fared worse on time-use factors compared to not attrits. Overall, it looks like that the ones who did not participate in endline were worse in baseline academic indicators. However, balance checks in Table 3 show that attrition is not selectively different in treatment vs control and across both treatments.

Any selective attrition across treatment and control is likely to bias the _true_ treatment effect upwards if the students who left the control group would (retrospectively) have been selectively _better_

> 21The difference of 0.19 Z-score is significant with a p-value _<_ 0.001.

> 22See Dahlbom _et al._ (2011), Croson & Gneezy (2009) and Bengtsson _et al._ (2004) for a review on gender differences in

> 23There are 5% more girls than boys in the overestimating sample. This difference is significant with a p-value _<_ 0.001.

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_performers_ at endline than treatment group students and/or vice versa. To test the gravity of this potential concern, we use the bounding exercise suggested by Lee (2009) and called “Lee-Bounds”. In this exercise, we artificially impute the attrits in treatment and control groups selectively in the direction which may have caused an upward bias and measure the required magnitude of selective artificial imputation to render the treatment and control differences statistically insignificant. In our context, we start making treatment selectively weaker by imputing values which generate lower than the observed (non-attrit) treatment mean by a specific factor. Simultaneously, we make the control stronger by imputing values which generate higher than the observed control mean by the same factor. The imputations are conducted along with associated treatment effects until the treatment effect becomes statistically insignificant (p-value > 0.1). Table A-5 shows results from three such imputations.

We conclude from this exercise that students who attrit from study groups have to be 80–110% different across treatment and control (smarter in control versus the treatment and/or vice versa) for there to be an upward bias in the results. Going by the previous discussion in this section, since the attrits performed relatively poorer on academic indicators compared to non-attrits and that the attrition does not seem to be statistically different across both groups, it is unlikely that attrits would have been selectively smarter in control (i.e. selectively worse in treatment group) by a factor of 80–110% at the endline.

# **5 Discussion and Conclusion**

This study conducted a large-scale field experiment in Zanzibar to evaluate the impact of goal-setting on the academic performance of secondary school students. We find sizable and statistically significant impacts on important behavioral outcomes such as time use, effort, and self-discipline which are likely to enter students’ education production function. Contrary to the promising results from research from social psychology, we _do not_ find an impact on Math test scores in our context. In particular, Morisano _et al._ (2010) find more than half a standard deviation increase in grades for upper-year students at McGill University. Similarly, Schippers _et al._ (2015) find that goal-setting significantly reduces inequalities in achievement if implemented early in students’ academic careers.<sup>24</sup> However, our findings are consistent with recent experimental studies on _goal-settings_ and _nudges_ that also _do not_ find any impact on test scores (see Dobriyoni _et al._ (2017); Oreopoulos _et al._ (2018); and Oreopoulos & Petronijevic (2019)). Similar to Oreopoulos _et al._ (2018), we find that both treatments led to a significant change in students’ time use behavior, but this positive change did not translate into improvements in academic outcomes. An important difference is the the study by Oreopoulos _et al._ (2018) was conducted in a developed country. Also, our analysis of goal-setting reveals that a large fraction of students in the treatment schools had set very high goals compared to their baseline performance _(less realistic goals)_ , which may have resulted in sub-optimal levels of effort that do not align well with their ability.

In the second treatment, when we combine goal-setting with a recognition award, we find a weaker impact on outcome measures. We find this result consistent with both theoretical and empirical evidence on extrinsic motivations crowding out intrinsic motivations, in a context in which the utility from outcomes and gains have a stronger moral and/or social component attached to them.<sup>25</sup> Efforts to improve academic performance have a higher degree of morality attached to them compared to efforts towards competitions or at the workplace. Also, receiving social recognition for putting higher

> 24See Morisano _et al._ (2010) for an overview.

> 25Bowles (2008) shows that incentives may be counterproductive and may crowd out intrinsic motivations when incentives may reduce dignity, morality and autonomy.

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efforts towards academic performance may be construed as less moral or less prosocial.<sup>26</sup> Hence, it is plausible that such social comparisons might have diluted the goal-setting _bite_ of the intervention.

The heterogeneity analysis suggests that the goal-setting intervention had a slightly larger impact on female students than their male counterparts. Most importantly, we find that the intervention relatively helped students belonging to the weaker socio-economic backgrounds who are likely catching up from lower levels of performance, and have larger room to improve. Building on our initial observation of students overestimating their baseline performance, we find that students who overestimate their baseline performance demonstrate slightly better performance in outcomes. We also find that low performing students overestimate their baseline scores, which is consistent with the Dunning-Kruger effect.<sup>27</sup> Higher improvements among overestimating students can also be linked to higher risk-taking ability which may result in better innovation (Hershleifier _et al._ (2012)). It can also be explained as a higher marginal improvement from low baseline levels as compared to their high baseline level counterparts.

Overall, the results from this study suggest that while goal-setting seems to move in the right direction by positively impacting effort, time-use, and self-discipline, the movement in these behavioral outcomes is not enough to have a discernible impact on actual academic performance. This study also highlights the importance of having an accurate idea of own performance/ability to set realistic goals, and being able to achieve those. This study was conducted _at scale_ encompassing the entire area of Zanzibar, and the results, therefore, circumvent the issues related to external validity and potential mismatches between trials at a small scale and large scale-ups.

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- Royer, H., Stehr, M., & Sydnor, J. 2015. Incentives, Commitments, and Habit Formation in Exercise: Evidence from a Field Experiment with Workers at a Fortune-500 Company. _American Economic Journal: Applied Economics_ , **7(3)** , 51–84.

- Samek, A. 2016. Gifts and Goals: Behavioral Nudges to Improve Child Food Choice at School. _CESR-Schaeffer Working Paper No. 2016-007_ .

- Schippers, M.C., West, M.A., & Dawson, J.F. 2015. Team Reflexivity and Innovation: The Moderating Role of Team Context. _Journal of Management_ , **41(3)** , 769–788.

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- Wiese, B.S., & Freund, A.M. 2011. Goal progress makes one happy, or does it? Longitudinal findings from the work domain. _Journal of Occupational and Organizationall Psychology_ , **78(2)** , 287–308.

17
# **Tables & Figures**

Table 1: Sample Size at Baseline

||(1)|(2)|
|---|---|---|
|Study Group|No. of Schools|No. of Students|
|Control|62|7,105|
|Goal-Setting|64|5,962|
|Goal-Setting + Recognition|61|5,214|
|Total|187|18,281|

_Notes_ : This table reports the baseline sample size (both number of schools and number of students) for each of the study groups.

Table 2: Study Timeline

|Month/Year|Activities|
|---|---|
|January, 2016|Randomization and Designing Instruments|
|February, 2016|Baseline Data Collection + Baseline Tests + goal-setting|
|August, 2016|Goal Reminders to Students|
|October, 2016|Endline Data Collection + Endline Tests|

_Notes_ : This table shows the timeline of field activities, data collection and rollout of interventions.

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Table 3: Balance on Student Characteristics

|||Mean|(SD)|||P-Value||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||Overall|Control|GS|GS + R|GS vs. Control|GS + R vs. Control|GS vs. GS + R|
|Male|0.448<br>(0.004)|0.457<br>(0.006)|0.412<br>(0.006)|0.476<br>(0.007)|0.035|0.326|0.024|
|Age|16.603<br>(0.010)|16.612<br>(0.015)|16.555<br>(0.017)|16.643<br>(0.017)|0.48|0.68|0.599|
|Is the student repeating the current grade?|0.064<br>(0.002)|0.071<br>(0.003)|0.053<br>(0.003)|0.068<br>(0.004)|0.062|0.811|0.139|
|Whether father can read and write in English|0.679<br>(0.003)|0.683<br>(0.006)|0.706<br>(0.006)|0.641<br>(0.007)|0.364|0.16|0.101|
|Whether mother can read and write in English|0.53<br>(0.004)|0.532<br>(0.006)|0.548<br>(0.006)|0.507<br>(0.007)|0.592|0.485|0.525|
|Household asset index|0<br>(0.007)|-0.007<br>(0.011)|0.065<br>(0.010)|-0.065<br>(0.013)|0.428|0.543|0.383|
|Baseline English test z-score|0.043<br>(0.008)|0<br>(0.012)|0.121<br>(0.014)|0.011<br>(0.015)|0.182|0.911|0.377|
|Baseline Math test z-score|0.047<br>(0.008)|0<br>(0.012)|0.096<br>(0.013)|0.054<br>(0.015)|0.374|0.631|0.672|
|Spend more than 30 minutes in Math (baseline)|0.661<br>(0.004)|0.66<br>(0.006)|0.674<br>(0.006)|0.648<br>(0.007)|0.501|0.605|0.468|
|Absent at the endline exam|0.266<br>(0.003)|0.282<br>(0.005)|0.267<br>(0.006)|0.241<br>(0.006)|0.614|0.169|0.3203|
|Observations|18,281|7,105|5,962|5,214||||

_Notes_ : This table reports the balance test for various student level variables captured in baseline survey. Means, standard deviations and p-value of differences is reported by GS (Goal-Setting), GS + R (Goal-Setting + Recognition) and Control group. Standard errors are clustered at the level of school.
Table 4: Balance on School Characteristics

|||Mean|(SD)|||P-Value||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||Overall|Control|GS|GS + R|GS vs. Control|GS + R vs. Control|GS vs. GS + R|
|Total students in F2|132.595<br>(10.229)|143.86<br>(17.626)|132.964<br>(21.954)|120.768<br>(12.703)|0.699|0.289|0.631|
|Total qualifed teachers in F2|4.832|4.638|4.946|4.951|0.545|0.412|0.992|
||(0.184)|(0.262)|(0.435)|(0.275)||||
|Student-teacher ratio in F2|28.228|29.151|28.054|27.341|0.812|0.626|0.876|
||(1.696)|(2.646)|(3.767)|(2.592)||||
|Does this school have two shifts?|0.602<br>(0.046)|0.583<br>(0.083)|0.667<br>(0.076)|0.553<br>(0.082)|0.463|0.793|0.311|
|Form 2 pass rate in 2015 for English|50.99|50.108|51.582|51.351|0.861|0.88|0.978|
||(3.342)|(5.724)|(6.088)|(5.849)||||
|Form 2 pass rate in 2015 for Math|44.153|38.796|47.474|47.194|0.33|0.309|0.974|
||(3.477)|(6.023)|(6.445)|(5.541)||||
|Form 2 pass rate in 2015 for Science|48.731|46.033|47.703|53.597|0.85|0.329|0.504|
||(3.411)|(5.43)|(6.901)|(5.428)||||
|Average teaching experience in month|150.491<br>(6.724)|139.479<br>(13.055)|149.542<br>(9.991)|162.742<br>(11.91)|0.541|0.19|0.397|
|Observations|187|62|64|61||||

_Notes_ : This table reports the balance test for various school level variables captured in baseline survey. Means, standard deviations and p-value of differences is reported by GS (Goal-Setting), GS + R (Goal-Setting + Recognition) and Control group.
# Table 5: Main Results

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Dependent Variable:|Time-use<br>Index|Efort<br>Index|Self-discipline<br>Index|Confdence<br>Index|Aspirations<br>Index|Math<br>Z-score|English<br>Z-score|
|Goal-Setting|0.113***|0.106**|0.090**|0.018|0.018|0.056|0.062|
||(0.042)|(0.048)|(0.043)|(0.034)|(0.039)|(0.071)|(0.065)|
|Goal-Setting + Recognition|0.100**|0.051|0.069|-0.010|-0.026|0.026|0.065|
||(0.044)|(0.055)|(0.044)|(0.043)|(0.040)|(0.068)|(0.059)|
|Observations|12,715|11,908|13,049|12,981|12,145|13,426|13,426|
|Baseline Outcome Controlled|Yes|No|No|No|Yes|Yes|Yes|
|B-H Passed (Goal-Setting)|Yes|Yes|Yes|N/A|N/A|N/A|N/A|
|P-Value (Goal-Setting)|(0.009)|(0.027)|(0.035)|-|-|-|-|
|B-H Passed (Goal-Setting + Recognition)|Yes|N/A|N/A|N/A|N/A|N/A|N/A|
|P-Value (Goal-Setting + Recognition)|(0.025)|-|-|-|-|-|-|

_Notes_ : This table reports the impact of interventions of key outcomes of interest: Time-use Index, Effort Index, Self-Discipline Index, Confidence Index, Aspirations Index, Math test score, and English test score. Construction of these indices is discussed in Section 2. All the results are subjected to Benjamin-Hochberg correction and last set of rows of the table reports if they pass the correction criteria (P-values in parenthesis). Standard errors are clustered at the level of school. *p<0.1; **p<0.05; ***p<0.01.
Table 6: Components of Time-use Index

|||Panel A||
|---|---|---|---|
||(1)|(2)|(3)|
|Dependent Variable:|Studying/Homework<br>Outside School|Household work|Sleeping|
|Goal-Setting|0.042|-0.056|-0.087**|
||(0.040)|(0.038)|(0.038)|
|Goal-Setting + Recognition|0.014<br>(0.043)|-0.106***<br>(0.040)|-0.039<br>(0.041)|
|Observations|13,273|13,250|13,219|
|Baseline Outcome Controlled|Yes|Yes|Yes|
|||Panel B||
||(1)|(2)|(3)|
|Dependent Variable:|Games/Leisure time<br>Outside school|Studying extra for<br>Endline exam|Studying Math<br>Outside school|
|Goal-Setting|-0.043|0.022|0.067*|
||(0.027)|(0.048)|(0.039)|
|Goal-Setting + Recognition|0.004|0.023|0.059|
||(0.031)|(0.059)|(0.047)|
|Observations|13,258|13,310|13,241|
|Baseline Outcome Controlled|Yes|Yes|Yes|

_Notes_ : This table reports the result of estimating equation 1 on individual components of Time-use Index. All the components are discussed in Section 2. Standard errors are clustered at the level of school. *p<0.1; **p<0.05; ***p<0.01.

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Table 7: Components of Effort Index

|||Panel A||
|---|---|---|---|
||(1)|(2)|(3)|
|Dependent Variable:|Studied regularly|Tried to do well|Tried to get<br>better score|
|Goal-Setting|0.055|0.058|0.069*|
||(0.038)|(0.040)|(0.039)|
|Goal-Setting + Recognition|0.033|0.032|0.050|
||(0.040)|(0.043)|(0.044)|
|Observations|12,418|12,903|13,028|
|Baseline Outcome Controlled|No|No|No|
|||Panel B||
||(1)|(2)|(3)|
|Dependent Variable:|Participated in<br>class discussions|Prepared Lessons|Organized<br>school work|
|Goal-Setting|0.076**|0.046|0.056*|
||(0.034)|(0.034)|(0.032)|
|Goal-Setting + Recognition|0.056|0.000|0.012|
||(0.042)|(0.042)|(0.034)|
|Observations|12,601|11,902|12,490|
|Baseline Outcome Controlled|No|No|No|

_Notes_ : This table reports the result of estimating equation 1 on individual components of Effort Index. All the components are discussed in Section 2. Standard errors are clustered at the level of school. *p<0.1; **p<0.05; ***p<0.01.

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Table 8: Components of Self-Discipline Index

||(1)|(2)|(3)|
|---|---|---|---|
|Dependent Variable:|Be good at<br>what I do|Be disciplined<br>and push myself|Finish whatever<br>I begin|
|Goal-Setting|0.078*|0.025|0.077**|
||(0.043)|(0.032)|(0.036)|
|Goal-Setting + Recognition|0.074*|0.011|0.052|
||(0.041)|(0.037)|(0.035)|
|Observations|13,343|13,339|13,152|
|Baseline Outcome Controlled|No|No|No|

_Notes_ : This table reports the result of estimating equation 1 on individual components of SelfDiscipline Index. All the components are discussed in Section 2. Standard errors are clustered at the level of school. *p<0.1; **p<0.05; ***p<0.01.

Table 9: Components of Confidence Index

||(1)|(2)|(3)|
|---|---|---|---|
|Dependent Variable:|Feel confdent<br>in Exam|Feel confdent<br>with friends|Feel confdent<br>when interacting<br>with teachers|
|Goal-Setting|0.016|-0.024|0.045|
||(0.031)|(0.034)|(0.029)|
|Goal-Setting + Recognition|-0.024|-0.032|0.031|
||(0.039)|(0.030)|(0.037)|
|Observations|13,177|13,169|13,392|
|Baseline Outcome Controlled|No|No|No|

_Notes_ : This table reports the result of estimating equation 1 on individual components of Confidence Index. All the components are discussed in Section 2. Standard errors are clustered at the level of school. *p<0.1; **p<0.05; ***p<0.01.

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Table 10: Components of Aspiration Index

||(1)|(2)|(3)|
|---|---|---|---|
|Dependent Variable:|Having high goals<br>and expectations|Do not expect<br>much in future|Desires further<br>Education|
|Goal-Setting|0.042|0.016|0.000|
||(0.033)|(0.043)|(0.039)|
|Goal-Setting + Recognition|0.015|0.043|-0.034|
||(0.037)|(0.046)|(0.044)|
|Observations|12,763|12,485|13,340|
|Baseline Outcome Controlled|Yes|Yes|Yes|

_Notes_ : This table reports the result of estimating equation 1 on individual components of Aspiration Index. All the components are discussed in Section 2. Standard errors are clustered at the level of school. *p<0.1; **p<0.05; ***p<0.01.

25
Table 11: Heterogeneous Effects - By Gender

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Dependent Variable:|Time-use<br>Index|Efort<br>Index|Self-discipline<br>Index|Confdence<br>Index|Aspiration<br>Index|Math<br>Z-score|English<br>Z-score|
||||Panel|A: Male Studen|ts|||
|Goal-Setting|0.079*|0.089|0.053|0.051|0.010|0.010|0.045|
||(0.047)|(0.062)|(0.049)|(0.047)|(0.047)|(0.087)|(0.069)|
|Goal-Setting + Recognition|0.071|0.055|0.020|0.031|-0.028|0.009|0.037|
||(0.055)|(0.067)|(0.052)|(0.050)|(0.052)|(0.096)|(0.067)|
|Observations|5,132|4,854|5,315|5,305|4,930|5,454|5,454|
|Baseline Outcome Controlled|Yes|No|No|No|Yes|Yes|Yes|
||||Panel B|: Female Stude|nts|||
|Goal-Setting|0.130***|0.107**|0.115**|-0.010|0.020|0.102|0.089|
||(0.049)|(0.054)|(0.051)|(0.038)|(0.047)|(0.070)|(0.070)|
|Goal-Setting + Recognition|0.121**|0.046|0.106**|-0.041|-0.025|0.041|0.085|
||(0.051)|(0.059)|(0.048)|(0.049)|(0.045)|(0.053)|(0.059)|
|Observations|7,583|7,054|7,734|7,676|7,215|7,972|7,972|
|Baseline Outcome Controlled|Yes|No|No|No|Yes|Yes|Yes|

_Notes_ : This table reports the heterogeneity (by gender) of the impact of interventions on main outcomes of interest: Time-use Index, Effort Index, Self-Discipline Index, Confidence Index, Aspiration Index, Math test score, and English test score. Construction of these indices is discussed in Section 2. Panel A has the sample of only male students while Panel B reports the results for the sample of female students. Standard errors are clustered at the level of school. *p<0.1; **p<0.05; ***p<0.01.
Table 12: Heterogeneous Effects - By Parents English Language skill

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Dependent Variable:|Time-use<br>Index|Efort<br>Index|Self-discipline<br>Index|Confdence<br>Index|Aspiration<br>Index|Math<br>Z-score|English<br>Z-score|
|||Pan|el A: Neither pa|rent can read|/write Engli|sh||
|Goal-Setting|0.232***|0.136*|0.121*|0.007|0.077|0.038|0.065|
||(0.060)|(0.074)|(0.067)|(0.057)|(0.058)|(0.066)|(0.067)|
|Goal-Setting + Recognition|0.136**|0.079|0.064|-0.012|-0.023|-0.002|0.059|
||(0.057)|(0.087)|(0.067)|(0.060)|(0.063)|(0.063)|(0.060)|
|Observations|2,940|2,737|3,036|3,026|2,782|3,150|3,150|
|Baseline Outcome Controlled|Yes|No|No|No|Yes|Yes|Yes|
|||Pan|el B: Either pa|rent can read|/write Englis|h||
|Goal-Setting|0.079*|0.097**|0.081*|0.020|0.000|0.061|0.059|
||(0.045)|(0.048)|(0.042)|(0.036)|(0.041)|(0.076)|(0.069)|
|Goal-Setting + Recognition|0.097**|0.046|0.074*|-0.006|-0.022|0.039|0.075|
||(0.049)|(0.054)|(0.044)|(0.048)|(0.039)|(0.075)|(0.063)|
|Observations|9,775|9,171|10,013|9,955|9,363|10,276|10,276|
|Baseline Outcome Controlled|Yes|No|No|No|Yes|Yes|Yes|

_Notes_ : This table reports the heterogeneity (by english language skill of Parents) of the impact of interventions on main outcomes of interest: Time-use Index, Effort Index, Self-Discipline Index, Confidence Index, Aspiration Index, Math test score, and English test score. Construction of these indices is discussed in Section 2. Panel A reports the results for the sample where neither parents can read/write in English. Panel B reports the results for the sample where either parents (at least one) can read/write in English. Standard errors are clustered at the level of school. *p<0.1; **p<0.05; ***p<0.01.
Table 13: Heterogeneous Effects - By Wealth

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Dependent Variable:|Time-use|Efort|Self-discipline|Confdence|Aspiration|Math|English|
||Index|Index|Index|Index|Index|Z-score|Z-score|
|||P|anel A: Below|Median Wealth|at Baseline|||
|Goal-Setting|0.151***|0.115*|0.123**|-0.025|-0.034|0.012|0.054|
||(0.049)|(0.065)|(0.054)|(0.045)|(0.049)|(0.063)|(0.059)|
|Goal-Setting + Recognition|0.132***|0.064|0.099*|0.007|-0.039|-0.013|0.064|
||(0.044)|(0.071)|(0.055)|(0.049)|(0.046)|(0.061)|(0.052)|
|Observations|6,451|5,984|6,632|6,586|6,118|6,852|6,852|
|Baseline Outcome Controlled|Yes|No|No|No|Yes|Yes|Yes|
|||P|anel B: Above|Median Wealth|at Baseline|||
|Goal-Setting|0.071|0.098**|0.056|0.052|0.056|0.089|0.048|
||(0.049)|(0.045)|(0.044)|(0.040)|(0.045)|(0.087)|(0.078)|
|Goal-Setting + Recognition|0.068|0.036|0.038|-0.030|-0.009|0.072|0.074|
||(0.062)|(0.052)|(0.047)|(0.056)|(0.053)|(0.090)|(0.072)|
|Observations|6,264|5,924|6,417|6,395|6,027|6,574|6,574|
|Baseline Outcome Controlled|Yes|No|No|No|Yes|Yes|Yes|

_Notes_ : This table reports the heterogeneity (by baseline wealth level of student’s household) of the impact of interventions on main outcomes of interest: Time-use Index, Effort Index, Self-Discipline Index, Confidence Index, Aspiration Index, Math test score, and English test score. Construction of these indices is discussed in Section 2. Panel A reports the results for sample which is below the median level of wealth at baseline and Panel B reports the results for sample which is above the median level of wealth at baseline. Wealth is measured using the standardized index of the sum of all the self-reported assets owned by the household. Standard errors are clustered at the level of school. *p<0.1; **p<0.05; ***p<0.01.
Table 14: Main Results - By Gap Between Expected and Actual Baseline Score (Degree of _Overestimation_ )

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Dependent Variable:|Time-use<br>Index|Efort<br>Index|Self-discipline<br>Index|Confdence<br>Index|Aspiration<br>Index|Math<br>Z-score|English<br>Z-score|
|||Panel A:|Overestimating|Baseline Perform|ance (Gap|_≥_points)||
|Goal-Setting|0.096**|0.116**|0.103**|0.018|-0.008|0.092|0.010|
||(0.046)|(0.052)|(0.046)|(0.035)|(0.040)|(0.069)|(0.067)|
|Goal-Setting + Recognition|0.074|0.087|0.079*|0.022|-0.023|0.041|0.024|
||(0.047)|(0.057)|(0.044)|(0.043)|(0.045)|(0.068)|(0.064)|
||(0.055)|(0.067)|(0.052)|(0.050)|(0.052)|(0.096)|(0.067)|
|Observations|9,908|9,254|10,186|10,114|9,421|10,480|10,480|
|Baseline Outcome Controlled|Yes|No|No|No|Yes|Yes|Yes|
||Pa|nel B: Not|Overestimating|Baseline Perfor|mance (Gap|_<_ 10 point|s)|
|Goal-Setting|0.133***|0.074|0.061|-0.003|0.065|0.033|0.156**|
||(0.047)|(0.051)|(0.044)|(0.039)|(0.043)|(0.100)|(0.074)|
|Goal-Setting + Recognition|0.145**|0.014|0.068|-0.053|-0.021|0.040|0.127*|
||(0.058)|(0.057)|(0.045)|(0.045)|(0.051)|(0.094)|(0.071)|
|Observations|8,621|8,111|8,830|8,805|8,276|9,085|9,085|
|Baseline Outcome Controlled|Yes|No|No|No|Yes|Yes|Yes|

_Notes_ : This table reports the heterogeneity (by level of overestimation of baseline performance) of the impact of interventions on main outcomes of interest: Time-use Index, Effort Index, Self-Discipline Index, Confidence Index, Aspiration Index, Math test score, and English test score. Construction of these indices is discussed in Section 2. Panel A has the sample of only male students while Panel B reports the results for the sample of female students. Standard errors are clustered at the level of school. *p<0.1; **p<0.05; ***p<0.01.
<!-- Start of picture text --> ~<br>Gel<br>2<br>2N<br>©<br>fa)<br>°<br>-2 0 2 4 6<br>Baseline Math ZScore<br>—— Control ——— Goal Setting<br>———- Goal Setting + Recognition<br><!-- End of picture text -->
Figure 3: Distribution of Goals: Goal-Setting + Recognition <!-- Start of picture text --> 0 5 10 15 20<br>Set Goal (Out of 20)<br>.5<br>.4<br>.3<br>Density<br>.2<br>.1<br>0<br><!-- End of picture text --> _Notes_ : This figure shows the distribution of set goals (out of 20) for all the students in the Goal-Setting + Recognition treatment arm.

Figure 4: Goal _minus_ Actual Baseline Score: Goal-Setting Arm <!-- Start of picture text --> −10 0 10 20<br>Goal (out of 20) − Baseline Math Score (out of 20)<br>.15<br>.1<br>Density<br>.05<br>0<br><!-- End of picture text --> _Notes_ : This figure shows the distribution of the difference between the set goal and actual baseline score for all the students in the Goal-Setting only treatment arm.

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Figure 5: Goal _minus_ Actual Baseline Score: Goal-Setting + Recognition <!-- Start of picture text --> −10 0 10 20<br>Goal (out of 20) − Baseline Math Score (out of 20)<br>.15<br>.1<br>Density<br>.05<br>0<br><!-- End of picture text --> _Notes_ : This figure shows the distribution of the difference between the set goal and actual baseline score for all the students in the Goal-Setting + Recognition treatment arm. Figure 6: Expected _minus_ Actual Baseline Score: Goal-Setting Arm <!-- Start of picture text --> −10 0 10 20<br>Expected Baseline − Actual Baseline<br>.15<br>.1<br>Density<br>.05<br>0<br><!-- End of picture text --> _Notes_ : This figure shows the distribution of the difference between the actual and expected baseline score for all the students in the Goal-Setting only treatment arm.

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Figure 7: Expected _minus_ Actual Baseline Score: Goal-Setting + Recognition <!-- Start of picture text --> −10 0 10 20<br>Expected Baseline − Actual Baseline<br>.15<br>.1<br>Density<br>.05<br>0<br><!-- End of picture text --> _Notes_ : This figure shows the distribution of the difference between the actual and expected baseline score for all the students in the Goal-Settings + recognition treatment arm.

33
<!-- Start of picture text --> L ‘Check to confirm if it is a Treatment 1 school. VERY IMPORTANT<br>2 Give a summary of the exercise to the class teacher and ask for their assistance in carrying it out with the Form 2<br>stadents.them" You can say: “We are doing a short exercise about student motivations and discussing personal goais with<br>3.- theGathersurvey.all studentsEstablish intoa therelaxedclassroomenvironmentand ask themwhere to students take a seat. Thankare not affaidthe studentsof speakingfor theirup andtime askingafter the testquestions.and<br>Adiiress the entire class about the importance of setting goals and achieving them You can use the following script:<br>importance“Thank you of so settingmuch forgoal:your andtimetarget: and patience. and strivingHopetoward:you enjoyedachievingyour thesesodas. targets.We wouldSett l i ngke togoai:talkis10a fmdamentalyou about the<br>‘Reapcomponentyou motivatedto long-termwhen success.you feel Goalslike giving help upyou focus and allocateyour time and resources efficiently, and they can<br>‘4, Tntvoduce the concept of Personal Best targets to students. You can say “When serting target: or goals for yourself it<br>is important to make sure that the target iz your personal be:t. For example, if you cored 85 out of 100 in your test,<br>‘that means that you are capabie af performing that well. Now when you set a goal for yourself for next year. should<br>‘pushit beio more thanyourself to85score or less?higher.Yes, This it shouldhigherbe scoremore.will beBecauseyouryouPersonal have onceBezt,achieved meaning85 itout isofsomething 100, it meansyou youhaveshouldyet 10<br>L Ask students to stand up from their seat Tell the students to stand up and reach for the ceiling as high as they can. As<br>* they are reaching. say, “Now reach three inches higher.” As students push themselves to reach higher, say, “Jthougir<br>‘you were already reaching az high a: you could. Where did you get the extra three inche:?” Have the students sit<br>‘down and respond to you about the folowing question “Whar is the difference between doing a good job and doing<br>‘your personal best?” Take answers from 1 or 2 stadents. Ifno one answers, say: “When you do your personal best, it<br>4 even better than good. Ir iz the best that you are capable of doing”<br>to<br>3, Tint the first page ofthe questionnaire. You can say: “We now want you to think about what you want to Be when<br>* you grow up. Write this down on the sheet we provided you. [4fter 2 minutes] Make sure no one has any questions.<br>“Now we want you to think about how the knowledge of Math can heip you become better at this profession. You can<br>write as many things as you can think of, and they may be very smail reasons. For example a carpenter needs to be<br>good at Math to take accurate measurement: ” Ask the teacher to go around the class and see if anyone is struggling<br>to answerthe question,<br>4, | Moving on to the goal setting questions: “We now want you to think about how weil you think you performed on the<br>Math test you just took. The Math test had 20 questions so it was out of 20 marks. Think about how many questions<br>You think you got right, and that should be your final score. Be az honest as possible. These scores will not be shared<br>‘with your teachers or peers. Now we would lize you to think about the exercise we did in trying to touch the ceiling,<br>and how you pushed yourseif to reach even higher after you set a higher goal for yourself. Think about how you smady<br>for‘yourzelf,your MathYou canclass do thisand bytest, first andlooking comeatuphowwithwellway: you thinkof improvingyou performedyourselfontotheachieveMath testan thateven youhigherjust targettook, andfor<br><!-- End of picture text -->

<!-- Start of picture text --> ‘set a personal best (PB) target for next year's Math test which will be very similar to this test. Your personal best<br>‘score should also be out of 10°.<br>5. Once the students have written down their Personal Best target/goals in Math. ask the class teacher to go over each<br>- theirstudent strengthsand check to ifcome thetotargetsa more reasonableare reasonable.goalIfto not, theachieve teachersfor them should spend time with the student and go over<br>¢, Forthe Math final question, say: “Now in the next question, write down what you cored in your Form I Math final<br>school exam. The exam that you took was out of 100 marks. If you don’t remember exactly what you scored, give an<br>‘estimate, and we will check with your teachers”<br>1 ‘Now ask students to fill out the rest of the questionnaire and let you know if they have any questions.<br>3, Collect the students goal setting questionnaires. Before you finish say: “It is important that you remember your PB<br>‘target score (out of 20) for any upcoming mathematics exams. Now that you have set this PB target, we encourage<br>‘you to remember this target as you prepare for your exams, since these tests will help you achieve your target score<br>in the newt math test. During the school year, we will periodically go over the goals you set today, and see where you<br>stand in your challenge to achieve them by the end of the year TM<br>9. ‘Thank the students and excuse them<br>0, Ask teachers to put up the poster in the classroom or school as a reminder to the students. Encourage teacher to sive<br><!-- End of picture text --> <!-- Start of picture text --> 1 Check to confirmif it is a Treatment 2 school. VERY IMPORTANT<br>2 Give a summary of the exercise to the class teacher, and ask for their assistance in carrying it out with the Form<br>7 2 students. You can say: “We are encouraging students to set higher goals for themselves. We want them to<br>think about their score in last year’s Math exam and set a personal best target for next year that is higher than<br>that score. We will then track their performance to observe if it actually does improve. At the end of the year,<br>those that meet their goals will be awarded at a grand ceremony at school.”<br>3, Camry out steps 2-8 same as that for Treatment 1 school<br>4 Collect the students goal setting questionnaires. Before you finish say: “It is important that you remember your<br>"PB target score for any upcoming mathematics exams. Now that you have set this PB target, we encourage you<br>to remember this target as you prepare for your exams, since these tests will help you achieve your target score<br>in the math final exam. During the school year, we will periodically go over the goals you set today, and see<br>where you stand in your challenge to achieve them by the end of the year. At the end of the school year,<br>students who meet their target will receive a certificate of recognition to be rewarded at a grand ceremony<br>in the presence of their teachers, school peers, and some role models. Those that exceed their goals will<br>receive a medal of “outstanding performance” at the ceremony, and those that don’t meet their target<br>‘will not receive any awards.”<br>=, _Thank the students and excuse them.<br>6 Ask teachers to put up the poster in the classroom or school as a reminder to the students. Encourage teacher to<br>° give reminders to students about their goal setting every month.<br><!-- End of picture text -->
# **Appendix: Tables**

Table A-1: Overestimating and Overambitious Students

||**Overa**|**mbitious**|
|---|---|---|
|**Overestimating**|Yes|No|
|Yes|0%|60%|
|No|5.8%|34%|

_Notes_ : This table reports the cross tabulation of the fraction of treated students (in both treatment arms) who are _overestimating_ their baseline performance and/or set overambitious goals.

Table A-2: Main Results (Pooled Treatments)

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Dependent Variable:|Time-use|Efort|Self-Discipline|Confdence|Aspiration|Math|English|
||Index|Index|Index|Index|Index|Z-score|Z-score|
|Treatments Pooled|0.107***|0.080*|0.080**|0.005|-0.003|0.042|0.063|
||(0.037)|(0.044)|(0.037)|(0.032)|(0.034)|(0.062)|(0.053)|
|Observations|12,715|11,908|13,049|12,981|12,145|13,426|13,426|
|Baseline Outcome Controlled|Yes|No|No|No|Yes|Yes|Yes|

_Notes_ : This table reports the impact of pooled interventions (both Goal-Setting and Goal-Setting + Recognition groups pooled together) on main outcomes of interest. Standard errors are clustered at the level of school. *p<0.1; **p<0.05; ***p<0.01.

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Table A-3: Parent’s and Teacher’s Efforts

||(1)|(2)|
|---|---|---|
|Dependent Variable:|Parent’s Efort<br>Index|Teacher’s Efort<br>Index|
|Goal-Setting|0.004|0.038|
||(0.048)|(0.078)|
|Goal-Setting + Recognition|-0.015|0.071|
||(0.046)|(0.090)|
|Observations|13,183|13,113|

_Notes_ : This table reports the impact of interventions on Parents Effort Index and Teacher’s Effort Index. Standard errors are clustered at the level of school. *p<0.1; **p<0.05; ***p<0.01.

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Table A-4: Attrition and Baseline Characteristics

||(1)|(2)|
|---|---|---|
|Baseline Variable|Coefcient|P-Value|
|Gender (Girl = 1)|-0.157<br>|0|
||[.017]||
|Living with parents = 1|-0.001<br>[.009]|0.931|
|Mother’s Occupation is Farming|-0.024<br>[.015]|0.123|
|Mother is housewife|-0.001<br>[.008]|0.882|
|Mother’s occupation (Other non farming)|0.003<br>[.011]|0.78|
|Father’s occupation is Farming|-0.021<br>[.012]|0.1|
|Father has no occupation|0.004<br>[.003]|0.11|
|Mother can read and write in English = 1|-0.011<br>[.012]|0.379|
|Father can read and write in English = 1|0.011<br>[.012]|0.336|
|Number of people in household|-0.005<br>[.011]|0.683|
|Asset Index|0.08<br>[.024]|0.001|
|Student repeating current grade = 1|-0.015<br>[.005]|0.002|
|Student remembers last year’s Math score = 1|0.002<br>[.015]|0.896|
|Student studied and did homework outside school|-0.127<br>[.024]|0|
|Helped in household|-0.029<br>[.024]|0.222|
|Played games/spend time with friends outside school|0.159<br>[.026]|0|
|Time spent studying math outside school|-0.08<br>[.022]|0|
|Wants to pursue further education after graduating school|-0.043|0|
|Math score at Baseline|-0.551<br>[.097]|0|
|Expected math score at Baseline|-0.261|0.037|
||[.123]||

_Notes_ : This table reports the predictors of attrition using baseline characteristics of students. The dependent variables are baseline characteristics and the independent variable is a dummy taking the value 1 if student attrited at endline and 0 otherwise. Standard errors are clustered at the level of school. *p<0.1; **p<0.05; ***p<0.01.

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# Table A-5: Lee Bounds on Treatment Effect

|||1st Imput|ation||2nd Imput|ation||3rd Imput|ation||4th Imput|ation|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Dependent Variable:|Time-use|Efort|Self-Discipline|Time-use|Efort|Self-Discipline|Time-use|Efort|Self-Discipline|Time-use|Efort|Self-Discipline|
|Goal-Setting|0.068**|0.055*|0.054*|0.059*|0.049|0.050|0.059*|0.042|0.045|0.054|0.036|0.041|
||(0.033)|(0.030)|(0.030)|(0.033)|(0.033)|(0.032)|(0.033)|(0.033)|(0.031)|(0.033)|(0.033)|(0.030)|
|Goal-Setting + Recognition|0.059*|0.026|0.042|0.051|0.023|0.038|0.052|0.020|0.035|0.046|0.017|0.031|
||(0.034)|(0.036)|(0.032)|(0.034)|(0.038)|(0.034)|(0.035)|(0.036)|(0.033)|(0.036)|(0.037)|(0.030)|
|Observations|17,337|18,281|18,281|17,337|18,281|18,281|17,337|18,281|18,281|17,337|18,281|18,281|
|Baseline Outcome Controlled|Yes|NA|NA|Yes|NA|NA|Yes|NA|NA|Yes|NA|NA|
|Shift Factor|0.4|0.4|0.4|0.45|0.45|0.45|0.5|0.5|0.5|0.55|0.55|0.55|

_Notes_ : This table reports the results of estimating lee bounds on the set of main outcomes which showed significant movements i.e. Time-use Index, Effort Index, and Self-Discipline Index. Section 4.2 explains the analytical process in detail. While the imputation of attrits is done starting at 0.05 shift factor for each group, due to space constraints, this table reports only four such imputations ranging from 0.4 to 0.55. *p<.1; **p<.05; ***p<.01.