metadata: authors: 0: author: Goyal,Sangeeta
1: author: Pandey,Priyanka
2: author: Sundararaman,Venkatesh
count: India
docna: 0: docna: Public participation, teacher
            accountability, and school outcomes : findings from baseline
            surveys in three Indian states
docty: Policy Research Working Paper
subtopic: Educational Sciences,Educational Institutions & Facilities,Gender and Development,Hydrology,Effective Schools and Teachers
teratopic: Education
abstracts: cdata!: This paper presents findings from
            baseline surveys on student learning achievement, teacher
            effort and community participation in three Indian states,
            Karnataka, Madhya Pradesh and Uttar Pradesh. Results
            indicate low teacher attendance and poor student learning.
            Parents and school committees are neither aware of their
            oversight roles nor participating in school management.
            However, there is substantial heterogeneity in outcomes
            across states. Karnataka has better student and teacher
            outcomes as well as higher levels of community awareness and
            participation than the other two states. The authors find
            substantial variation in teacher effort within schools, but
            most observable teacher characteristics are not associated
            with teacher effort. One reason for low teacher effort may
            be lack of accountability. Regression analysis suggests low
            rates of teacher attendance are only part of the problem of
            low student achievement. The gains in test scores associated
            with higher rates of attendance and engagement in teaching
            are small in the states of Madhya Pradesh and Uttar Pradesh,
            suggesting teachers themselves may not be effective.
            Ineffective teaching may result from lack of accountability
            as well as poor professional development of teachers.
            Further research is needed to examine not only issues of
            accountability but also professional development of teachers.
geo_reg_key: 517198,517212,517191
repnme: repnme: Public participation, teacher
            accountability, and school outcomes : findings from baseline
            surveys in three Indian states
display_title: Public participation, teacher
            accountability, and school outcomes : findings from baseline
            surveys in three Indian states
listing_relative_url: /research/2008/11/10017179/public-participation-teacher-accountability-school-outcomes-findings-baseline-surveys-three-indian-states
topicv3: Teachers Management m1326119 1523,Water m1326522 137,Teacher Absenteeism m1327035 1528,Education m1327048 470,Gender m1327621 615
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Education
no_of_pages: 39
author_keys: 0: author_key: 168173
1: author_key: 712819
2: author_key: 437359
model_extractions: WPS4777 Policy ReseaRch WoRking PaPeR 4777 Public Participation, Teacher Accountability, and School Outcomes Findings from Baseline Surveys in Three Indian States _Priyanka Pandey Sangeeta Goyal Venkatesh Sundararaman_ The World Bank South Asia Region Human Development Department November 2008
Policy ReseaRch WoRking PaPeR 4777

# **Abstract**

This paper presents findings from baseline surveys on student learning achievement, teacher effort and community participation in three Indian states, Karnataka, Madhya Pradesh and Uttar Pradesh. Results indicate low teacher attendance and poor student learning. Parents and school committees are neither aware of their oversight roles nor participating in school management. However, there is substantial heterogeneity in outcomes across states. Karnataka has better student and teacher outcomes as well as higher levels of community awareness and participation than the other two states. The authors find substantial variation in teacher effort within schools, but most observable teacher characteristics are not associated with teacher effort. One reason for low teacher effort may be lack of accountability. Regression analysis suggests low rates of teacher attendance are only part of the problem of low student achievement. The gains in test scores associated with higher rates of attendance and engagement in teaching are small in the states of Madhya Pradesh and Uttar Pradesh, suggesting teachers themselves may not be effective. Ineffective teaching may result from lack of accountability as well as poor professional development of teachers. Further research is needed to examine not only issues of accountability but also professional development of teachers.

This paper—a product of the Human Development Department, South Asia Region—is part of a larger effort in the department to promote rigorous studies which can inform policy. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at ppandey@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
# **Public Participation, Teacher Accountability, and School Outcomes:**

## **Findings from Baseline Surveys in Three Indian States**

Priyanka Pandey, Sangeeta Goyal and Venkatesh Sundararaman<sup>∗</sup>

> ∗ South Asia Human Development, World Bank. This work benefited from generous financial support from the EPDF trust fund and by Government of the Netherlands through the BNPP program.  We are also grateful to the Nike Foundation for generous financial support. We thank Nazmul Chaudhury, Amit Dar, Lant Pritchett and Michelle Riboud for very helpful comments. We are grateful to the Ministry of Human Resource Development and the respective Departments of Education in Karnataka, Madhya Pradesh and Uttar Pradesh for extending their cooperation.
# **1. Introduction**

In recent years, learning outcomes have gained increasing importance in the policy debate on basic education in India. Recent findings reveal that although about 93 percent of Indian children in age group 6-14 years are in school, learning achievements are low. According to the ASER survey, 67 percent of children in grades 3 to 5 cannot read a simple text (ASER, 2005-2006). The NCERT (2006) baseline surveys across Indian states find average scores between 40 and 60 percent on curriculum based math and language tests. The problem of poor quality of outcome is not a problem of India alone or of education sector alone. A number of developing countries have a dismal record on the delivery of basic services like education, health and sanitation despite the fact that governments as well as donor agencies have channeled significant resources into these services (World Development Report, 2004).

Poor learning in schools may be, in part, due to high absence rates among teachers. A view that has gained consensus is that the mechanisms of accountability in public service delivery are often weak resulting in dysfunctional services. School teachers and health workers, among other public workers, have high rates of absenteeism in many countries. Chaudhury et. al. (2006) document provider absence in schools in several developing countries including India. Absence ranges from 11 to 27 percent among primary school teachers.

Interventions that provide physical resources alone are not enough to improve outcomes if workers delivering the service do not perform as expected. With the aim of increasing the accountability of public workers, various Indian states have decentralized control over local public services to local communities. The _Sarva shiksha abhiyan_ which is a nation wide government scheme initiated in 2001 to universalize quality education envisages increasing accountability of schools to the community through greater involvement of village education committees and parent-teacher associations. Nevertheless, high teacher absence rates recorded in recent studies confirm that teachers continue to have low levels of accountability and motivation.

The lack of accountability may be partly attributed to the fact that the communities are largely uninformed about the controls that have been devolved to them, as found in recent surveys. This study is designed to carry out a randomized evaluation of an information campaign that provided information to communities about their roles and responsibilities in school management. It covers three Indian states, Karnataka (KA), Madhya Pradesh (MP) and Uttar Pradesh (UP).

This paper presents findings from the study’s baseline surveys in 2006 on students’ learning achievement, teachers’ effort and community participation. The results from the impact evaluation of the information campaign are reported in a separate paper (Pandey et. al., 2008).

We present learning achievement in the context of the minimum level of learning outlined by government of India. We report on the gain in achievement across grade 2
cohorts, the variation in achievement between schools and within schools, and the characteristics of schools and teachers that are associated with student performance.

Results indicate low teacher attendance and poor student learning. Parents and school committees are neither aware of their oversight roles nor participating in school management. However, there is substantial heterogeneity in outcomes across states. Karnataka has better student and teacher outcomes as well as higher levels of community awareness than the other two states.

There is substantial variation in teacher effort within schools and many observable teacher characteristics are not related to teacher effort. However, regression analysis suggests low rates of teacher attendance are only part of the problem of low learning achievement. Higher teacher attendance may not be enough to increase learning substantially. Even when teachers are teaching, they are not effective in MP and UP. The gains in test scores associated with higher rates of attendance and engagement in teaching are small in these two states. Ineffective teaching can result from both lack of accountability and poor professional development of teachers. Research is needed to examine and evaluate teachers’ training and teaching processes inside the classroom.

Section 2 describes the context and background for the study. Section 3 describes the methodology and section 4 provides the results. Section 5 summarizes and discusses the key findings.

# **2. Background and Context for the Study**

Since the mid 1990s, Indian states have decentralized control over local public services to the immediate communities. The 73<sup>rd</sup> amendment to the Indian constitution in 1992 made it mandatory for Indian states to devolve control over public services and over funds for these services to the local government (called the _gram panchayat_<sup>_1_</sup> ). However it was left up to the states as to how much control to devolve. Various states in India have devolved control over services to different extents.

In parallel, another dimension of devolution has been that a number of Indian states trying to expand education rapidly have enacted policies to recruit contract teachers, often hired by local communities. These teachers are being hired at lower salaries and on more flexible, short-term contracts. These changes make it possible to hire larger numbers of teachers within a given fiscal envelope. In some cases, parents and community members participate in both their selection and performance monitoring (such as, attendance). This is intended also to increase teachers’ accountability to local communities. A brief description of the oversight devolved to the communities in the states is as follows.

> 1 A _gram panchayat_ is the lowest administrative unit consisting of two to three revenue villages on average. The elected village government ( _gram panchayat_ council with the _gram pradhan_ as its head) is formed at the _gram panchayat_ level.

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In UP, a village education committee (VEC) is mandatory in every _gram panchayat_ . There is one VEC for all the primary and junior government schools in the _panchayat_ . The VEC typically has 5 members. It is headed by the _gram pradhan_ (who is the elected head of the village government). Other members include the senior most head teacher among the schools, and three parents of children in these schools. The _pradhan_ and the head teacher jointly operate the school account. The school account receives annual grants for repair and maintenance, school development, teaching learning material (TLM), money for school dress, money for civil works- school building, rooms, toilet, drinking water etc if authorized and contract teachers’ salary. The _gram panchayat_ account which is cosigned by the VEC head receives scholarship funds and mid day meal funds. The VEC is supposed to manage and monitor funds coming to the school account and to the _gram panchayat_ account (funds earmarked for the school), determine how to use and give consent for use of these funds, request additional funds if civil work etc are required, keep record of accounts, and be involved in selection of contract teachers (called _shiksha mitra_ ). An additional contract teacher can be hired if the pupil-teacher ratio in the school exceeds 40, up to a maximum of two contract teachers and up to a maximum ratio of 3:2 of regular to contract teachers. For selection of contract teachers, VECs are required to follow state guidelines outlining eligibility criterion (18-35 years of age, minimum education of class 12, preferably a resident of the _gram panchayat_<sup>2</sup> , and if there are reservations by caste, gender) and are expected to rank applicants in order of qualification (grades obtained in class 12, 10). The contract is for a 10 month period and VEC decides whether to renew the contract to the next year. With a two-thirds majority, the VEC can remove the contract teacher anytime during this contract.

In MP, a parent teacher association (PTA) is mandatory in every school and parents of all children enrolled in the school are members. The PTA has an executive committee which runs the PTA, though all parents are supposed to participate in decision making. The school headmaster is the secretary of the PTA and a parent is the chair. The executive committee has up to 14 members who elect the PTA chair from among them. The PTA chair and the head teacher jointly operate the school account. The school account receives annual grants for repair and maintenance, for school development, TLM, money for school dress, money for civil works- school building, rooms, toilet, drinking water etc and money for mid day meal. The PTA manages and monitors funds coming to the school accounts, determines how to use and gives consent for use of these funds, requests additional funds if civil work etc are required, keeps record of accounts, verifies or signs on all teachers attendance in order for their monthly salary to be released. The contract teachers (known as _samvida shikshak_ ) are hired on a 3 year renewable contract by the block _panchayat_ . Applicants have to satisfy eligibility criteria (education, residence, reservation if any by caste, gender) and are expected to be selected according to qualification (grade attained in an exam for all applicants, grades obtained previously, and interview with the _block panchayat_ ). There are also former contract teachers (known as _shiksha karmi_ ) who have been made permanent recently, except that their salaries are much lower than that of regular civil service teachers. Regular teachers and _shiksha karmi_ s are no more being recruited.

> 2 If the VEC cannot find a suitably qualified candidate within the _gram panchayat_ , then it can consider candidates from neighboring _panchayats_ .

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In Karnataka, every school has a mandatory School development and monitoring committee (SDMC). The SDMC members include the head teacher of the primary school, elected members of the village government and parents of children. One of the parents is the chair of SDMC. The head teacher and SDMC chair jointly operate the school account. The types of funds received in the school account are similar to those in the case of MP. SDMC is supposed to call a meeting of all parents every 3 months to discuss trimester exam results. There are no contract teachers in Karnataka. The SDMC can inspect the schools and complain to the district or block education office.

Previous work suggests community members and members of local government do not often know about the state mandated controls they have over teachers and school accounts. A recent study in UP finds that VEC members are not informed of their roles and responsibilities and that VECs are not functional (Banerjee et. al., 2006). The baseline findings reported below also show that in MP and UP in particular, VEC or PTA members are unaware of their roles, meetings are rarely held and PTAs are involved neither in verification of teachers’ attendance nor in managing school accounts.

# **3. Methods**

This is a cluster randomized controlled trial of 610 _gram panchayats_ across the three states, randomly allocated to receive or not receive the information campaign. Cluster randomized sample size calculations were based on a 5 percent significance level and 80 percent power. In each state four districts were chosen purposefully, matched across states in terms of the literacy rate. Within a district, two blocks were randomly chosen. A block is an administrative unit between a district and a village. Within the two blocks, 25 villages were randomly chosen to be in the treatment group and another 25 randomly chosen to be in the control group. This gives a total of 100 control villages and 100 treatment villages per state in MP and UP. In Karnataka, an additional set of treatment villages were added that received a second treatment- information and advocacy campaign. The number of villages in each of the three cells in Karnataka (Control, Treatment 1 (information), Treatment 2 (information plus advocacy) is 70.

One randomly selected public primary school per _gram panchayat_ was chosen to be in the sample. All teachers teaching grades 1 to 5 are included. In MP and UP, 45 randomly chosen students per school (15 each from grades 2-4) and in Karnataka 30 randomly chosen students per school (15 each from grades 4-5) are in the sample.

**Timeline** A baseline survey was administered between February-April 2006 in MP and UP and between August-September 2006 in Karnataka.

# **Outcome indicators in baseline surveys**

In the baseline, four unannounced visits were made per school to collect information on the following.

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- Teacher attendance and activity. Activity is a measure of whether a teacher is actively engaged in teaching when the survey team arrives unannounced. Teacher attendance is 1 if teacher is present in school, 0 otherwise. Teacher activity is 1 if teacher is teaching, writing on the board, supervising written work, teaching by rote, 0 if teacher is absent, chatting, sitting idle/standing outside classroom, keeping order in classroom but not teaching, doing other non teaching work. There are four observations per teacher on attendance and activity. Averaging across visits at the teacher level, these give the fraction of visits a teacher was present (or engaged in teaching).  Averaging the attendance (or activity) at the school level across the four visits gives the average fraction of teachers present (or engaged in teaching activities) in a school over the visits.

- Test scores of sample students based on a competency and curriculum based language and math tests that lasted approximately 20 minutes per child. The language test was a test of reading and writing skills. The math test was a test of number recognition, addition, subtraction, multiplication and division. Tables 2 to 7 present grade wise details of test items and performance on each item. The total test score in each grade is constructed separately for language and math tests as the percent of correct answers.  In MP and UP tests took place at the end of school year in 2006 while in Karnataka, these were held 4 months later at the beginning of school year in 2006.  Students receiving the same test in Karnataka are 4 months ahead in the year and hence in the next grade. For example, the test given to grade 4 students at end of school year in MP and UP was given to class 5 students at beginning of school year in Karnataka. And the test given to grade 3 students at end of school year in MP and UP was given to class 4 students at beginning of school year in Karnataka.

- Interview of parents of sample students on their knowledge about existence of school committees.

- Interview of VEC, PTA and SDMC members on participation in school oversight, knowledge of roles and responsibilities and knowledge about school funds.

- School facilities survey.

Additional data were collected on socio-economic characteristics of students such as parents’ education, caste, and wealth and on characteristics of teachers such as age, education, experience, wealth, type of contract and training.

# **4** .

## **Results from Baseline Surveys**

Table 1 presents a summary of the sample variables including learning outcomes and teacher effort. Karnataka stands out in comparison to MP and UP in that it has substantially higher learning levels, higher teacher effort, greater community awareness and participation (the latter is reported in later tables).

## **4.1 Learning Achievement**

**What is the benchmark?** We use the minimum level of learning (MLL) framework recognized by the government of India as a benchmark for the minimum that a child in a

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given grade should know. The competencies tested for each grade in the survey fall either within or below the set of competencies listed by the MLL for the grade. As an example, the MLL for language specifies that a child in grade 1 should be able to read simple sentences and write simple 3 syllable words. A child in grade 2 should be able to read short paragraphs as well as write dictated sentences. In Math, a child in grade 2 should recognize numbers up to 100, should be able to do two digit additions with carryover and subtractions with borrowing. According to the framework, at least 80 percent of children are expected to be able to do at least 80 percent of the competencies for the grade. The framework can be found at <u>www.education.nic.in/cd50years/r/2S/Book2S.htm.</u>

**Total score** Figures 1 and 2 present the percent correct score in the three states. Given our MLL benchmark, learning achievement as measured by the percent correct responses is low in all three grades in MP and UP and relatively higher in Karnataka. In grade 4, the mean percent correct score in mathematics is 23 for UP, 29 for MP and 69 for Karnataka (denoted by KA in figures and tables). The mean percent correct score in language for grade 4 is 27 for UP, 33 for MP and 75 for Karnataka.

# **Figure 1 Mean score in mathematics**

<!-- Start of picture text --> 1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>Class 4 (class 5- Class 3 (class 4- Class 2<br>KA) KA)<br>UP MP KA<br>Figure 2 Mean score in language<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>Class 4 (class 5- Class 3 (class 4- Class 2<br>KA) KA)<br>UP MP KA<br>Percent score<br>writing<br>Percent score- reading and<br><!-- End of picture text -->

**Figure 2 Mean score in language**

**Item wise performance** Tables 2-7 present item wise performance of children in language and math in grades 4-2 for MP and UP at end of school year and in grades 4-5 in language and math for Karnataka at beginning of school year.

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There are two key points that come out of these tables. First, on extremely basic competencies such as recognizing words on the language test and recognizing numbers or solving addition or subtraction on the math test, the sample of children in MP and UP perform dismally and children in Karnataka do way better. In UP, only 46 percent of children after four years of schooling can read 3 or more words from a list of five. And barely 22 percent in UP and 33 percent in MP at the end of grade 4 can read a simple sentence compared to 73 percent in Karnataka.

The percent of children answering correct is much higher in Karnataka compared item wise to MP and UP. However, as one goes to the slightly higher level competencies such as writing a sentence or solving a division problem, the performance of children in Karnataka falls compared to their own performance on the more basic competencies.

|**Table 2**<br>**What do grade 4 c**<br>Percentage childrencorrect|**hildren**<br>UP|**know in**<br>MP|**languag**<br>KA|**e***<br>Grade level at<br>the<br>competen<br>specified<br>in<br>framework|which<br>cy<br>is<br>MLL|
|---|---|---|---|---|---|
|Can read at least 3 of 5 words(without matra)|46|54|92|I & II||
|Can read at least 3 of 5 words(with matra)|33|38|89|I & II||
|Can read simple sentence|22|33|73|II||
|Can write at least 2 of 3 words(without matra)|31|35|78|I||
|Can write at least 2 of 3 words(with matra)|16|22|68|I||
|Can write short sentence|13|19|47|II||

|**Table 3**<br>**W**|**hat do grade**|**4 children**|**know in**|**mathem**|**atics***|
|---|---|---|---|---|---|
||||||Grade level at which<br>the<br>competency<br>is<br> <br> <br>|
|Percentage childrencorrect|Test item|UP|MP|KA|specified<br>in<br>MLL<br>framework|
|Number recognition<br>2digit||31|46|93|I|
|Number recognition<br>3 digit||16|19|84|III|
|2 digit addition without<br>carryover|62+35|53|64|91|II|
|2 digit addition with carry<br>over|85+46|26|42|80|III|
|2 digit subtraction without<br>borrowing|54-32|41|48|84|II|
|2 digit subtraction with<br>borrowing|84-39|18|20|63|II|
|Multiply2 digit by1 digit|43 х 8|15|25|59|III|
|Multiply2 digit by2 digit|35 х 24|10|14|40|IV|
|Divide 2 digit by1 digit|54÷6|14|19|60|III|
|Divide 3 digit by1 digit|585÷9|8|11|41|IV|

*End of school year for grade 4 in MP/UP and start of school year for class 5 in KA

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Second as figures 3-4 and tables 2-7 show, the gain across grade cohorts on any given competency is small for MP and UP. In UP, 15 percent of children in grade 3 can do addition with carryover compared to 26 percent of children in grade 4. In MP, the percentages of children who can do subtraction with borrowing are 11 percent in grade 3 and 20 percent in grade 4. The gain in Karnataka also seems small on a given competency, although what’s going on in Karnataka may be different. A large fraction of the sample children in Karnataka know the tested competencies (which are fairly basic for the grades) and so there may not be much room for an improvement.

**Figure 3   Gain in reading across grades**

<!-- Start of picture text --> Reading across grade cohorts<br>100%<br>Read sentence<br>80%<br>and w ords<br>60% Read w ords<br>40%<br>20% Nothing<br>0%<br>Figure 4 Gain in mathematics across grades<br>Selected math competencies across grade<br>cohorts<br>100<br>80<br>Subtraction w ith<br>60<br>borrow ing or more<br>40<br>Addition w ith carryover<br>20<br>0 Nothing<br>UP-grade 3UP- grade 4MP-grade 3MP-grade 4KA- grade 4KA-grade 5<br>UP-grade 3UP- grade 4MP-grade 3MP-grade 4KA- grade 4KA-grade 5<br>Percent children<br>Percent children<br><!-- End of picture text --> The gain in performance across grade cohorts is similar across competencies, although somewhat smaller for the higher level competencies. If the sample of children is similar across grades, these gains will be close to the actual learning gains on the concepts tested as a child progresses through grades. This then presents a fairly stark picture that despite the approximate 220 days of schooling that children are supposed to receive in a year of school, the learning that is taking place is not much at all in MP and 9
UP where as many as 75-86 percent of children at the end of grade 3 cannot read a simple sentence and this percentage continues to be 67-78 for children at the end of grade 4.

## **4.2 Learning Decomposition**

## **4.2.1 Are Observed Student and Family Characteristics Correlated with Performance?**

Table 8 presents results from grade wise linear regression with school fixed effects, where the dependent variable is the percent correct score on language and math tests, respectively. School fixed effects will control for observed and unobserved characteristics of the school and the village that are correlated with learning achievement. Student characteristics on the right hand side are age, gender, caste, education of parents and whether family’s own land is above the sample median.

The regression equation is as follows for student _i_ in school/village _j_ in block _k. Yijk_ is the test score (the percent correct score) in language or math. _Xijk_ is a vector of student _i_ ’s observed characteristics: age, gender, caste (two dummy variables for whether student belongs to general caste, other backward classes (OBC) or scheduled caste (SC)), whether mother is literate, whether father is literate, whether land owned by the household is above median land owned. _fjk_ is the vector of school fixed effects. ε _ijk_ is the error term representing the unobserved variables correlated with test scores.

# _Yijk  = a +  bXijk + fjk +_ ε _ijk_

A number of the observed student characteristics in UP, a few of these in MP and almost none in Karnataka are significantly correlated with test scores. In UP, student’s age, gender, caste, parents’ education and family wealth are all significant and sizeable in magnitude in the test score regressions (Table 8 and figures 5-6). Boys score 6 percentage points higher than girls. Students’ belonging to high caste (i.e. general caste which is neither OBC nor SC/ST) have 5-8 percentage points higher score and those with above median wealth have 3-4 percentage points higher score. Students’ with a literate mother have an 8-10 percentage points higher score and those with a literate father have a 7-8 percentage point higher score.

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# **Figure 5 Adjusted score gap in mathematics**

<!-- Start of picture text --> Grade 4<br>UP<br>0.15<br>** * MP<br>** *<br>0.10 ** KA<br> **<br>0.05 * *<br>0.00<br>High Male Mother Father Wealth<br>-0.05 caste literate literate<br>** p value below 1 percent, * p value below 5 percent<br>gap<br>Percent point score<br><!-- End of picture text --> In MP, students’ gender, caste and mother’s education are significant variables in the test score regressions (Table 8 and figures 5-6). An increase of one year in age decreases language score by 2 percentage points but not so in math where the age variable is insignificant. This may reflect the fact that a number of students in the sample districts of MP migrate temporarily every year with their parents for employment. Boys do better, scoring 5 percentage points higher than girls. Students from high caste (i.e. who are neither OBC nor SC/ST) have 4 percentage points higher score in math and ST students have a 7 percentage point lower score in language. Wealth variable is insignificant and so is father’s literacy. Those with a literate mother have 9-12 percent point higher score.

**Figure 6 Adjusted score gap in language**

<!-- Start of picture text --> Grade 4<br>UP<br>0.15 ** MP<br>** KA<br>0.10 ** **<br>**<br>0.05 * *<br>0.00<br>High Male Mother Father Wealth<br>-0.05 caste literate literate<br>** p value below 1 percent, * p value below 5 percent<br>Percent point score gap<br><!-- End of picture text --> In contrast, in Karnataka none of the observed student characteristics are significantly correlated with test scores. This may suggest that when schools are good, learning in school makes up for some of the household differences in learning and when students are not learning in school, family factors continue to matter more for learning outcomes. Results are generally similar for grades 3 and 2.

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# **4.2.2 Which of the School and Teacher Characteristics Are Correlated with Performance?**

Here we examine the correlation between school characteristics and test scores. Test score is regressed on observed student characteristics, school characteristics and block fixed effects. The regression equation is as follows where _Yijk_ is the test score of student _i_ in school/village _j_ in block _k_ : _Xijk_ is a vector of observed student characteristics (age, gender, caste, mother’s and father’s literacy, wealth), _Zijk_ is a vector of observed school and teacher characteristics. Since multi grade teaching seems to be widespread in the sample schools, it is difficult to identify a teacher to a class. Therefore, average teacher characteristics for the school are used on the right hand side. _Zijk_ consists of teacher-pupil ratio, index of school infrastructure, percentage of teachers with college degree, percentage of teachers with graduate degree, percentage of teachers with pre service training, percentage of male teachers, percentage of general caste teachers, mean age of teachers, mean years of experience, mean days of in service training in last school year, mean teacher attendance, mean teacher activity (see Table 1 for definition of these variables) and percentage of contract teachers. _fk_ are block dummy variables which control for block fixed effects and ε _ijk_ is the error term. Tables 9-12 present the results from these regressions.

In UP, teacher-pupil ratio is positively and significantly correlated with language and math test scores in all grades (Tables 9-10). An extra teacher is associated with 3 percentage point increase in scores. Teacher activity is positively and significantly correlated with language and math test scores in all grades. If 80 percent of the teachers were to become actively engaged in teaching, language and math scores would be higher by 7 and 8 percentage points respectively. Controlling for activity, teachers’ attendance is not significant in most regressions. Other characteristics of the school and teachers including teacher training are insignificant.

In MP, teacher activity is positively and significantly correlated with language and math test scores in all grades (Tables 11-12). If the percentage of teachers actively engaged in teaching increased by about 30 percentage points (the average percent of teachers actively engaged is 30 percent), the increase in grade 4 scores would be about 6 and 5 percentage points in language and math, in that order. The gains in grades 3 and 2 scores from a similar increase in teacher activity would be slightly higher. Most other characteristics of the school including teacher-pupil ratio and in service training are not significantly associated with test scores. Multi grade teaching is negatively correlated with scores in grades 3 and 2.

In Karnataka, teacher attendance is the only variable significantly correlated with both language and math test scores in all grades (Table 13). Other school characteristics, including teacher pupil ratio and teacher activity are not correlated with scores.

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The one school characteristic that is consistently and significantly correlated with learning achievement is teacher engagement in teaching in MP and UP and teacher presence in Karnataka. In all three states, most other school or teacher characteristics are not associated with test scores. In Karnataka 88 percent of teachers are present and of those present, almost 80 percent are actively engaged in teaching. Since most teachers who are present are teaching, this may explain why teacher attendance is significant in the score regressions and not teacher activity.

Based on the regression coefficients, we calculate that if about half the teachers were engaged in teaching in MP and UP i.e., teaching activity roughly doubled in magnitude, scores would be higher by 17-31 percent in math and language in the three grades. We would have expected an increase in teacher activity of this proportion to be associated with a bigger increase in score. One reason for a small effect is that teacher activity may not measure real teacher effort precisely. Another reason is that teachers may not be effective in classrooms in MP and UP. For example, if average teacher attendance and engagement in teaching in MP and UP were to be the same as in Karnataka and assuming all other factors influencing learning stayed constant, the average scores in grade 4 in MP and UP would still be below 40 percent, far from the average of 70-75 percent in Karnataka.

Although we have reported test score correlations (not causations) based on a cross section survey, our findings are consistent with international evidence on the impact of school and teacher characteristics on student test scores. A number of studies find no conclusive impact of class size on student performance (Hanushek, 1998; Lazear, 1999) consistent with our results from MP and KA. There is evidence however that the impact of policies on class size may depend on the level of education concerned and the absolute level of class sizes. Class size may matter when the average size is quite large, as in UP schools which have average class size of 70.

On the other hand, there is fairly robust evidence that of the school level variables which can be influenced by policy, factors to do with teachers and teaching are the most important influences on student learning. The broad consensus is that teacher quality is the single most important school variable influencing student learning (DarlingHammond, 2000; Rockoff, 2004; Rivkin, Hanushek and Kain, 2005). But what it is about teachers that matters is less well known. Researchers agree that many important aspects of teacher quality are not captured by the commonly used quality indicators such as education, experience, and subject matter knowledge. Two recent studies find that teacher attendance has a positive impact on test scores (Duflo, Hanna and Ryan 2007; Miller, Murnane and Willet, 2007). Broadly parallel to these findings, our results show that teacher effort is positively correlated with student performance, unlike other attributes of schools or teachers including education and experience that have no correlation.

# **4.2.3 How Much of the Variation in Test Scores Is between and within Schools?**

Regressing scores on school fixed effects gives the variation in scores attributable to variation in observed and unobserved characteristics across schools and villages. The 13
remaining variation is attributable to variation in within school characteristics. Schools and villages explain up to 20 percent of the variation in mathematics and language scores in grade 4 in UP, 40 percent in MP and 43 percent in Karnataka (Figure 7). This means a fairly large percent of the variation is within schools, larger in UP (about 72 percent) and smaller in KA (56 percent).<sup>3</sup> This finding is quite consistent with international evidence, although from developed countries, that the largest source of variation in student learning is attributable to what students bring to school (Rivkin, Hanushek and Kain, 2005; OECD report, 2005). The difference in the extent of within school variation across the states is in line with the finding above (in 4.2.1) that many observed student characteristics in UP, a few in MP and almost none in KA are significant in explaining the test scores.

# **Figure 7 Decomposing variations in grade 4 mathematics score between and within schools**

<!-- Start of picture text --> 100%<br>Remaining w ithin<br>80% school variation<br>60%<br>Observed<br>characteristics of the<br>40%<br>student<br>20% Betw een schools<br>and villages<br>0%<br>UP MP KA<br>Percent variation in scores<br><!-- End of picture text --> The within school variation in achievement is attributable to differences in students, classrooms characteristics, within school variation in teacher quality and unexplained variation in the data. However, observed family and student characteristics explain little of the variation in scores within schools. When the full set of observed student covariates are added to the test scores regression with school fixed effects, the explanatory power of the score regression goes up by 9 percent in UP, 3 percent in MP and a mere 1 percent in KA. This means that the observed dimensions of family background do not explain much of the variation in performance within schools but the unobserved dimensions such as motivation, ability and attitude possibly do.

# **4.3 Teacher Effort**

Since teacher effort is correlated with student performance unlike other attributes of the school, we look at the data to see which teachers work harder than others. While the data cannot shed light on why teacher effort varies across states or why some teachers are more motivated than others, it captures the variation in teacher effort due to observed characteristics of teachers which are commonly used by policymakers and school administrators as indicators of teacher quality.

> 3 The numbers are similar for grades 2 and 3.

14
Teacher attendance and engagement in teaching are low in both MP and UP and much higher in Karnataka. On average, 88 percent of teachers were present in KA, 65 percent in UP and 67 percent in MP. The average fraction of teachers present and actively engaged in teaching was 68 percent in KA, 25 percent in UP and 30 per cent in MP. In all the states, a high proportion of teachers are male and from the high caste. More than 50 percent of the teachers have a college education in MP and UP unlike Karnataka where 72 percent of the teachers have a grade 12 degree or less (Table 1).

Do we observe any variation in teacher effort by terms of appointment? Both MP and UP have a cadre of contract teachers. Contract teachers (called _shiksha mitra_ in UP and _samvida shikshak_ in MP) have significantly higher attendance and activity compared to regular teachers (Figures 8-9, Table 14).

# **Figure 8 Contract teachers versus regular teachers in UP**

<!-- Start of picture text --> Teacher effort- UP<br>0.8<br>0.6 All<br>0.4 Regular teachers<br>0.2 Contract<br>0<br>Attendance- % Activity- %<br>teachers present teachers engaged<br>in teaching<br>Percent present or active<br><!-- End of picture text -->

# **Figure 9 Contract teachers versus regular teachers in MP**

<!-- Start of picture text --> Teacher effort- MP<br>0.8<br>All<br>0.6<br>0.4 Regular teachers<br>0.2<br>Contract<br>0<br>Attendance- % Activity- %<br>Former contract -<br>teachers present teachers engaged<br>shiksha karmi<br>in teaching<br>Percent present or active<br><!-- End of picture text --> Contract teachers, however, differ from regular teachers on a number of characteristics- they tend to be younger, more educated, a greater fraction female, with fewer years of experience and much less likely to have received any pre service training (Table 15). These differences in attendance and activity remain significant after controlling for observed teacher characteristics and school fixed effects (Tables 16-17).

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# **4.3.1 What Are the Correlates of Teacher Presence and Engagement at Work?**

To examine which teacher attributes are associated with teacher effort, we regress teacher attendance and activity on teacher characteristics and school fixed effects. The regression equations are below. _Teacher attendanceijk_ is a dummy variable which is 1 if teacher _i_ in school/village _j_ in block _k_ was present on a given visit, 0 otherwise. Since there were four visits, there are four observations per teacher. _Teacher activityijk_ is a dummy variable which is 1 if teacher _i_ in school/village _j_ in block _k_ was present and actively engaged in teaching, 0 if the teacher is absent or engaged in non-teaching task (see bottom of table 1 for definition of activity). _Xijk_ is a vector of teacher characteristics that includes age, gender, caste, education, whether teacher has pre service training, number of years of service, number of days of in service training in last school year, whether teacher’s appointment is on a contract basis. _fjk_ is a vector of school fixed effects.

_Teacher attendanceijk_ = _a + bXijk + fjk +_ ε _ijk_ _Teacher activityijk_ = _a + bXijk + fjk +_ ε _ijk_ Tables 16-18 present the regression results. In UP, teachers more likely to be present are contract teachers, those without a college degree and those without a graduate degree. Teachers more likely to be engaged in teaching are contract teachers, those without a college or a graduate degree, younger and female teachers. Contract teachers’ attendance is higher by 10 percentage points and activity is higher by 7 percent points compared to regular teachers. Both these differences are significant at _p_ values below 5 percent. Pre-service and in service trainings are not significant in the regressions (Table 16).

In MP, teachers more likely to be present are younger in age. Teachers more likely to be active are contract teachers, younger, without a college degree and with fewer years of experience. Pre service and in service trainings are not significantly associated with teacher effort (Table 17). Contract teachers’ attendance is not significantly different but their activity is 10 percentage points higher compared to regular teachers with _p_ value below 5 percent.

In Karnataka, female teachers are more likely to be present. Teachers more likely to be actively engaged in teaching are female, older and those with a college degree. Those with a post graduate degree are less likely to be engaged in teaching compared to those with a high school degree (Table 18).

There are three main themes here. The first is that teachers who are more qualified in terms of measurable characteristics, for example those with more education or experience, are likely to put in less effort compared to the teachers who appear to be less qualified. This could have to do with the more qualified teachers having an elite status in the local community. The second theme is that most of the variation in teacher effort is within schools. The percentage of variation in teacher activity that is explained 16
by differences between schools and villages is 21 in MP, 17 in UP and 13 in KA. The third point is that in all of the states observed teacher characteristics explain less than 6 percent of the variation in teacher effort within schools. These observations are again consistent with other studies, although mainly from developed countries, that find substantial variation in teacher quality within schools and observed teacher characteristics explain very little of this variation in teacher quality (Rockoff, 2004; Rivkin, Hanushek and Kain, 2005; Aaroson, Barrow and Sander; 2007).

# **4.4 Are Local Communities Aware of Their Roles and Active in Managing Schools?**

Last we discuss an important strand of the survey that measures local participation in school management. In line with the variation in teacher effort, there is a parallel variation in community participation across the three states.

# **4.4.1 The Committees with Oversight Responsibilities**

Tables 19-24 present the awareness and participation of VEC, PTA and SDMC members in oversight of the school. A large proportion of committee members in all the three states stated not having received any training regarding their roles and responsibilities as members. Parent members of these committees had the lowest yes responses. About 20 percent of parent members reported receiving any training in Karnataka, compared to 8 percent in MP and 2 percent in UP.

In UP, most members seem to be aware that they are members of the VEC. However headmasters seem to be most informed about the roles and responsibilities of the VEC. Parent members of the VEC seem to be the least informed and participating the least (Tables 19-20). When asked to list the roles and responsibilities of VEC, 52 percent of the parent members could not list a single one. These members could name on average only one out of five VEC members.

# **Figure 10 UP    Village Education committee members’ responses**

<!-- Start of picture text --> 100<br>80<br>Headmaster<br>60<br>Pradhan<br>40<br>Parents<br>20<br>0<br>Whether Percent of other Whether VEC<br>received training VEC members meeting held in<br>named this school year<br>Percent<br><!-- End of picture text --> Like in UP, headmasters in MP seem to be most informed of the roles and responsibilities of the PTA (Tables 21-22). Parents seem to be the least informed of their 17
roles and participating the least. 58 percent of parent members of the PTA executive committee could not list a single role or responsibility. Parent members could name, on average, one out of ten members of the PTA.

**Figure 11 MP        PTA committee members’ responses**

<!-- Start of picture text --> 120<br>100<br>80 Headmaster<br>60 PTA Chair<br>40 Parents<br>20<br>0<br>Whether received Percent of other Whether PTA<br>training PTA excecutive meeting held in<br>members named this school year<br>Percent<br><!-- End of picture text --> Parent members of the SDMCs in Karnataka seem to be more informed about their roles and report participating in meetings and school inspections much more relative to their counterparts in MP and UP (Tables 23-24). Parent SDMC members could correctly name on average 4 out of 10 committee members (Figure 12 and Table 23).

**Figure 12 KA    SDMC members’ responses**

<!-- Start of picture text --> 100<br>80<br>Headmaster<br>60<br>SDMC chair<br>40<br>Parent/other members<br>20<br>0<br>Whether Percent of Whether SDMC<br>received other SDMC meeting held in<br>training members this school year<br>named<br>Percent<br><!-- End of picture text -->

# **4.4.2 Are Parents Aware of the School Oversight Committees?**

In UP only 8 percent of the parents interviewed were aware of the VEC. On the other hand in MP and Karnataka, 57 percent and 63 percent of the parents were aware of the respective committees. Parents were also asked if they could name the chair of the VEC, PTA or SDMC. The percentage of parents able to name the committee chair is highest in Karnataka, followed by MP and UP, where barely 2 percent of parents responded correctly.

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# **Figure 13   Parents awareness of school committees**

<!-- Start of picture text --> Parents response<br>70<br>60<br>50<br>UP<br>40<br>MP<br>30<br>KA<br>20<br>10<br>0<br>Is there a committee in your village to Do you know the name of this<br>look after the school committee's chair<br>Percent saying yes<br><!-- End of picture text -->

# **5. Summary**

Learning achievements differ substantially across states, low in both MP and UP and higher in Karnataka. On every single item, the percent of children answering correct is much higher in Karnataka compared to MP and UP. However in Karnataka as in the other two states, as we go to the slightly higher competencies such as writing a sentence or solving a division problem, students perform worse compared to their own performance on the more basic competencies.

More importantly, performance gains across grade cohorts are low on all competencies. The competencies tested fall within the set of competencies identified in the minimum levels of learning (MLL) framework recognized by the government of India. The norm of about 220 days of an additional school year is associated with an increase of 8-10 percentage points in the percent of children who can read or write simple text or solve basic math. To put it differently, if the goal is that every child should master at least the grade appropriate MLL competencies, then going from grade 3 to 4 in UP, 220 additional days of learning should lead to an increase in percent of children who can read simple text by 86 percentage points (since only 14 percent of grade 3 children can read). The actual gain from grade 3 to 4 in the data is only 8 percent points. This is 9 percent of the gain that should be taking place or equivalently worth only 20 additional school days.

Mirroring the differences in scores across states are systemic differences across states in teacher attendance and in the fraction of teachers who were observed to be engaged in teaching related activity. Karnataka has a much higher fraction of teachers present and engaged in teaching compared to MP and UP. In regressions of student test scores on school level variables, most physical inputs in the school are not correlated with scores in all three states whereas teacher engagement in teaching in MP and UP and teacher attendance in Karnataka are significantly and consistently associated with higher scores. However there is substantial variation in teacher effort within schools and many observable teacher characteristics are not related to teacher effort. This implies policies 19
that reward teachers on the basis of their credentials may not be effective in raising teacher effort.

One reason for low teacher effort in MP and UP may be low accountability of teachers in these states. Teachers may not be accountable and motivated in part because the communities do not have the capacity to hold them accountable. A large proportion of committee members in all three states had not received any training regarding their roles and responsibilities. Parent members of VEC and PTA are not actively participating in their oversight capacity and have very low levels of awareness regarding their roles and responsibilities. The headmasters seem to be executing most of the functions of VECs and PTAs. The communities at large are not aware of the existence of these committees. Whereas in Karnataka data show that even though only 20 percent of parent SDMC members stated receiving any training, they are aware and participating to a much greater extent compared to their counterparts in the other two states. SDMC members report more frequent committee meetings and school visits as well as greater knowledge of their oversight roles. The evaluation of the experiment undertaken as part of this study will provide some evidence on how much increase occurs in teacher effort when communities are better informed of their oversight roles.

However, low rates of teacher attendance and teaching activity are only part of the problem of low learning achievement. Even if the level of average teacher attendance and engagement in teaching in MP and UP were to be the same as in Karnataka or even if 100 percent of the teachers were present and engaged in teaching and assuming all other factors influencing learning stayed the same, the associated gain in language or math scores in the two states would be in the range of 10-11 percentage points based on the regression results in the paper. This is roughly the size of gain in performance across grade cohorts observed in the data. It would still keep the average scores 30-40 percentage points below Karnataka which leaves a large fraction of the children without any mastery of basic competencies.

What is going on in classrooms when teachers are present and teaching is obviously quite important. Although our data cannot shed light on any differences in the classroom processes across states, it does suggest that teachers in MP and UP may not be as effective. This could be related to issues of accountability as well as teachers’ professional development. Classroom processes and teacher training need to be researched. Experimental evaluations of innovations in these areas would be helpful in answering what works towards classrooms that actually deliver learning.

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|**Table 1**|**Summa**|**ry of key var**|**iables**||||
|---|---|---|---|---|---|---|
||**UP**<br>Mean|Std.Dev.|**MP**<br>Mean|Std.Dev.|**KA**<br>Mean|Std.Dev.|
|**Mean Student variables**|||||||
|% Correct score<br>Grade4- Math(class 5for KA)|.23|.29|.29|.29|.69|.28|
|Grade4- language (class 5for KA)|.27|.35|.33|.34|.75|.29|
|Grade 3- Math(grade4 for KA)|.17|.28|.26|.30|.67|.31|
|Grade 3- language (grade4 for KA)|.21|.32|.30|.35|.68|.31|
|Grade2- Math|.13|.26|.25|.31|-||
|Grade2- language|.20|.29|.31|.33|-||
|Age|8.72|1.61|8.95|1.57|10.08|.82|
|Gender(1 if male)|.49|.50|.51|.50|.52|.50|
|General caste (neither SC/ST, nor<br>OBC)|.15|.36|.20|.40|.04|.19|
|OBC|.40|.49|.31|.46|.51|.5|
|SC|.44|.50|.15|.36|.33|.47|
|ST|-|-|.34|.47|.13|.34|
|Mother literate|.21|.40|.13|.33|.37|.48|
|Father literate|.60|.49|.46|.50|.56|.50|
|Land owned (inacres)|1.13|1.65|2.50|4.27|3.33|4.54|
|**Mean School level characteristics**|||||||
|Enrollment|178|89|119|66|137|129|
|Pupil-teacher ratio|66|39|56|30|27|17|
|% schools withtoilet|.33|.47|.38|.49|.72|.45|
|% schools withdrinking water|.83|.38|.72|.45|.66|.48|
|% schools withplayground|.79|.41|.50|.50|.46|.50|
|% schools withelectricity|.01|.07|.07|.25|.59|.49|
|Numberofblackboards|3.75|1.77|3.32|1.84|5.84|3.81|
|**Mean Teacher level characteristics**|||||||
|Attendance (meanover 4visits)|.64|.48|.67|.47|.88|.32|
|Activity (meanover 4visits)|.25|.43|.30|.46|.68|.47|
|% ofcontract teachers|.41|.20|.15|.36|0|0|
|% of formercontract|-|-|.45|.50|-|-|
|Age (years)|38|14|39|9|39|8|
|Non-SC/ST(generalcaste+OBC)|.80|.40|.69|.46|.76|.43|
|Male|.60|.49|.80|.40|.59|.49|
|% withclass12degree|.42|.49|.48|.50|.72|.45|
|% withcollege degree|.32|.47|.30|.46|.26|.44|
|% withgraduate degree|.26|.44|.23|.42|.03|.16|
|% withpreservice training|.59|.49|.36|.48|.93|.26|
|Distance to work(km)|6|10|9|12|10|11|
|Teaching experience (years)|10.9|13|13.8|10|12|8|
|% doingmultigrade|.81|.39|.87|.34|.91|.29|
|Days of in service training|5.83|8|10.95|12|8.57|8|

**Definition of variables:** Teacher attendance= 1 if teacher present, 0 otherwise (and averaged over the four visits), teacher activity=1 if teacher actively engaged in teaching i.e., if teacher is teaching, writing on the board, supervising written work, teaching by rote, 0 if teacher is absent, chatting with others, sitting idle/standing outside classroom, keeping order in school/class but not teaching, doing some other work unrelated to teaching (and averaged over the four visits)

Index of infrastructure in school= sum of four indicator variables for whether school has water, toilet, playground, electricity and the total number of blackboards in school.

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|**Table 4**<br>**What do grade 3 children**|**know in**|**langua**|**ge***||
|---|---|---|---|---|
|Percentage children correct|UP|MP|KA|Grade level for the<br>competency as specified<br>in MLL|
|Can read at least 3 of 5 words without matra|38|47|86|I|
|Can read at least 3 of 5 words with matra|22|34|81|I|
|Can read sentence|14|26|61|I&II|
|Can write at least 2 out of 3 words without matra|31|36|76|I|
|Can write at least 2 out of 3 words with matra|13|21|60|I|
|Can write short sentence|10|18|40|I&II|

|**Table 5**<br>**What do grade 3 children know in mathematics***|
|---|

|Percentage childrencorrect|Test item|UP|MP|KA|Grade level for the<br>competency as specified<br>in MLL|
|---|---|---|---|---|---|
|Number recognition,20-40||29|50|87|I|
|Number recognition,40-99||20|35|83|I|
|2 digit addition without carryover|62+35|35|51|87|II|
|2 digit addition with carryover|53+39|15|26|69|II|
|2 digit subtraction without borrow|45-23|28|35|77|II|
|2 digit subtraction with borrow|54-36|9|11|50|II|
|Multiply1 digit by1 digit|6x8|12|20|66|II|
|Multiply2 digit by1 digit|42 x5|10|14|50|III|
|Divide 2 digit by1 digit|64÷ 8|9|11|54|III|

*End of school year for grade 3 in MP/UP and start of school year for class 4 in KA

# **<u>Table 6 What do grade 2 children know in language</u>**

|Percentage childrencorrect|UP|MP|Grade level for the competency as<br>specifiedin MLL|
|---|---|---|---|
|Alphabet recognition|32|53|I|
|Can read at least 3 of 5 words without matra|22|31|I|
|Can read at least 3 of 5 words with matra|11|19|I|
|Can write at least 2 of 3 words without matra|21|28|I|
|Can write at least 2 of 3 words with matra|7|15|I|

|**Table 7**<br>**What do grade 2**|**children kn**|**ow in m**|**athemat**|**ics**|
|---|---|---|---|---|
|Percentage childrencorrect|Test item|UP|MP|Grade level for the<br>competency as specified in<br>MLL|
|Number recognition,11-20||21|45|I|
|Number recognition,21-40||14|29|I|
|Writing2 digit numbers,21-40||14|31|I|
|1 digit addition|5+3|22|38|I|
|2 addition without carryover|26+43|17|27|II|
|1 digit subtraction|8-5|12|21|I|
|2 digit subtraction without borrowing|25-12|12|18|II|
|Multiply1 digit by1 digit|6x 4|7|14|II|
|Which is largest of three 2 digit<br>numbers|56, 84, 69|8|16|I|

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**Table 8 OLS: Are student characteristics correlated with scores?** **<u>Dependent variable is percent correct score, Grade 4</u>**

||**UP**||**MP**||**K**|**A**|
|---|---|---|---|---|---|---|
||**Hindi**|**Math**|**Hindi**|**Math**|**Kannada**|**Math**|
|Age|0.02|0.02|-0.02|-0.01|0.00|0.00|
||(0.01)**|(0.00)**|(0.00)**|(0.13)|(0.94)|(0.83)|
|Generalcaste|0.08|0.05|0.04|0.04|-|-|
||(0.00)**|(0.00)**|0.12|(0.05)*|-|-|
|OBC|0.05|0.04|-0.02|-0.02|-0.02|-0.02|
||(0.10)|(0.05)*|(0.39)|(0.36)|(0.13)|(0.22)|
|ST(scheduled tribe)|-|-|-0.07|-0.04|-0.04|-0.02|
||||(0.02)*|(0.16)|(0.10)|(0.30)|
|Wealth (1 if land above<br>median)|0.04|0.03|0.02|0.01|0.02|0.03|
||(0.01)**|(0.01)**|(0.34)|(0.68)|(0.18)|(0.07)|
|Gender(1 if male)|0.06|0.10|0.05|0.05|-0.01|0.00|
||(0.00)**|(0.00)**|(0.01)**|(0.00)**|(0.32)|(0.74)|
|Mother literate|0.10|0.08|0.12|0.09|0.02|0.01|
||(0.00)**|(0.00)**|(0.00)**|(0.00)**|(0.17)|(0.31)|
|Father literate|0.08|0.07|0.02|0.01|0.00|0.02|
||(0.00)**|(0.00)**|(0.18)|(0.24)|(0.86)|(0.08)|
|School/village fixed<br>effects|Yes|Yes|Yes|Yes|Yes|Yes|
|n|2579|2579|2321|2321|2176|2176|
|R-squared|0.28|0.27|0.34|0.43|.35|.44|

Robust p values in parentheses clustered at GP level, * significant at 5%; ** significant at 1%; 23
# **Table 9 OLS: Are school and teacher characteristics correlated with scores?** **<u>Dependent variable is percent correct score, Grade 4, UP</u>**

||**Hindi**|**Math**|**Hindi**|**Math**|**Hindi**|**Math**|
|---|---|---|---|---|---|---|
|**Teacher-pupil ratio**|**5.00**|**5.00**|**5.0**|**4.9**|**4.9**|**4.9**|
||**(0.00)****|**(0.00)****|**(0.00)****|**(0.00)****|**(0.00)****|**(0.00)****|
|Index of infrastructure|0.00|0.00|0.00|0.00|0.00|0.00|
||(0.91)|(0.70)|(0.88)|(0.62)|(0.85)|(0.62)|
|% non-sc teachers|-0.03|-0.05|-0.03|-0.05|-0.03|-0.05|
||(0.56)|(0.15)|(0.56)|(0.15)|(0.54)|(0.13)|
|% male teachers|0.01|0.01|0.01|0.01|0.01|0.02|
||(0.88)|(0.70)|(0.89)|(0.71)|(0.84)|(0.66)|
|% teachers with college deg|0.05|0.01|0.05|0.01|0.06|0.02|
||(0.27)|(0.72)|(0.29)|(0.83)|(0.23)|(0.62)|
|% teachers withgraduate deg|0.05|0.02|0.05|0.02|0.05|0.03|
||(0.25)|(0.56)|(0.25)|(0.57)|(0.21)|(0.47)|
|Av. Years of service|0.00|0.00|0.00|0.00|0.00|0.00|
||(0.68)|(0.60)|(0.63)|(0.76)|(0.51)|(0.86)|
|Av. days in service in lastyear|0.00|0.00|0.00|0.00|0.00|0.00|
||(0.91)|(0.41)|(0.92)|(0.40)|(0.96)|(0.30)|
|% teachers with pre service<br>training|0.01|-0.02|0.01|-0.02|0.07|0.04|
||(0.80)|(0.64)|(0.80)|(0.64)|(0.32)|(0.41)|
|% teachers doingmultigrade|0.00|0.03|-0.01|0.03|0.00|0.03|
||(0.94)|(0.53)|(0.94)|(0.54)|(0.95)|(0.51)|
|**% teachers actively engaged in**<br>**teaching (mean over 4 visits)**|**0.13**|**0.14**|**0.15**|**0.17**|**0.12**|**0.13**|
||**(0.05)***|**(0.01)***|**(0.05)***|**(0.00)****|**(0.09)**<sup>+</sup>|**(0.03)***|
|% teachers present in school<br>(mean over 4 visits)|-|-|-0.02|-0.05|-|-|
||-|-|(0.73)|(0.28)|-|-|
|% contract teachers|-|-|||0.10|0.11|
||-|-|||(0.26)|(0.10)<sup>+</sup>|
|Block fixed effect|Yes|Yes|Yes|Yes|Yes|Yes|
|n|2553|2553|2553|2553|2553|2553|

Robust p values in parentheses clustered at GP level, * significant at 5%; ** significant at 1%;<sup>+</sup> significant at 10%. Other controls include all student family background characteristics that are in table 8 and block fixed effects.

24
# **Table 10 OLS: Are school and teacher characteristics correlated with scores?** **<u>Dependent variable is percent correct score, Grades 3 and 2, UP</u>**

||**Hindi**<br>**Grade 3**|**Math**<br>**Grade 3**|**Hindi**<br>**Grade 3**|**Math**<br>**Grade 3**|**Hindi**<br>**Grade 2**|**Math**<br>**Grade 2**|**Hindi**<br>**Grade 2**|**Math**<br>**Grade 2**|
|---|---|---|---|---|---|---|---|---|
|**Teacher-pupil ratio**|**5.83**|**5.33**|**5.83**|**5.33**|**4.4**|**3.7**|**4.4**|**3.7**|
||**(0.00)****|**(0.00)****|**(0.00)****|**(0.00)****|**(0.00)****|**(0.00)****|**(0.00)****|**(0.00)****|
|Index of infrastructure|0.01|0.00|0.01|0.00|0.01|0.00|0.01|0.00|
||(0.16)|(0.33)|(0.16)|(0.31)|(0.20)|(0.60)|(0.20)|(0.55)|
|% non-sc teachers|-0.01|-0.03|-0.01|-0.03|-0.01|-0.02|-0.01|-0.02|
||(0.93)|(0.54)|(0.93)|(0.53)|(0.78)|(0.52)|(0.78)|(0.52)|
|% male teachers|0.03|0.01|0.03|0.01|0.04|0.00|0.04|0.00|
||(0.44)|(0.78)|(0.45)|(0.76)|(0.18)|(0.89)|(0.18)|(0.93)|
|% teachers with college<br>deg|0.09|0.03|0.09|0.03|0.09|0.06|0.09|0.06|
||(0.01)*|(0.34)|(0.02)*|(0.31)|(0.01)*|(0.06)<sup>+</sup>|(0.01)*|(0.06)<sup>+</sup>|
|% teachers with graduate<br>deg|0.05|0.04|0.05|0.04|0.05|0.04|0.05|0.05|
||(0.29)|(0.35)|(0.29)|(0.34)|(0.19)|(0.18)|(0.19)|(0.17)|
|Av. Years of service|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
||(0.38)|(0.84)|(0.40)|(0.91)|(0.97)|(0.83)|(0.95)|(0.73)|
|Av. days of in service<br>lastyear|0.00|0.00|0.00|0.00|0.00|-0.003|0.00|-0.003|
||(0.79)|(0.41)|(0.79)|(0.38)|(0.41)|(0.06)<sup>+</sup>|(0.44)|(0.05)*****|
|% teachers with pre<br>service training|0.04|-0.01|0.04|0.01|0.03|0.02|0.02|0.04|
||(0.40)|(0.91)|(0.47)|(0.84)|(0.53)|(0.59)|(0.61)|(0.31)|
|% teachers doing multi<br>grade|0.01|0.03|0.01|0.03|0.00|0.01|0.00|0.01|
||(0.84)|(0.44)|(0.84)|(0.43)|(0.93)|(0.74)|(0.93)|(0.73)|
|**% teachers actively**<br>**engaged in teaching**<br>**(mean over 4 visits)**|**0.11**|**0.10**|**0.11**|**0.10**|**0.13**|**0.10**|**0.13**|**0.10**|
||**(0.05)***|**(0.03)***|**(0.05)***|**(0.03)***|**(0.00)****|**(0.01)***|**(0.00)****|**(0.02)***|
|% contract teachers|-|-|0.00|0.03|-|-|-0.01|0.04|
||-|-|(0.99)|(0.65)|-|-|(0.88)|(0.45)|
|Block fixed effect|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|n|2673|2670|2673|2670|2697|2695|2697|2695|

Robust p values in parentheses clustered at GP level, * significant at 5%; ** significant at 1%;<sup>+</sup> significant at 10%. Other controls include all student family background characteristics that are in table 8 and block fixed effects.

25
# **Table 11 OLS: Are school/teacher characteristics correlated with scores?** **<u>Dependent variable is percent correct score, Grade 4, MP</u>**

||**Hindi**|**Math**|**Hindi**|**Math**|**Hindi**|**Math**|
|---|---|---|---|---|---|---|
|**Teacher-pupil ratio**|**1.11**|**0.22**|**0.96**|**0.14**|**1.09**|**0.20**|
||**(0.34)**|**(0.78)**|**(0.41)**|**(0.86)**|**(0.35)**|**(0.80)**|
|Index of infrastructure|0.01|0.00|0.01|0.00|0.01|0.00|
||(0.12)|(0.72)|(0.10)<sup>+</sup>|(0.67)|(0.11)|(0.69)|
|% non-sc teachers|-0.04|-0.04|-0.05|-0.05|-0.04|-0.04|
||(0.26)|(0.21)|(0.18)|(0.16)|(0.33)|(0.22)|
|% male teachers|-0.03|-0.04|-0.03|-0.04|-0.02|-0.04|
||(0.52)|(0.34)|(0.54)|(0.35)|(0.64)|(0.35)|
|% teachers with college deg|0.01|0.02|0.01|0.02|0.01|0.02|
||(0.80)|(0.55)|(0.79)|(0.55)|(0.81)|(0.56)|
|% teachers withgraduate deg|0.03|0.03|0.04|0.04|0.03|0.03|
||(0.57)|(0.40)|(0.51)|(0.35)|(0.60)|(0.43)|
|Av. Years of service|0.00|0.00|0.00|0.00|0.00|0.00|
||(0.29)|(0.37)|(0.29)|(0.37)|(0.27)|(0.29)|
|Av. days of in service lastyear|0.00|0.00|0.00|0.00|0.00|0.00|
||(0.48)|(0.41)|(0.44)|(0.43)|(0.46)|(0.43)|
|% teachers with pre service<br>training|-0.02|-0.04|-0.01|-0.03|-0.02|-0.03|
||(0.63)|(0.23)|(0.72)|(0.26)|(0.65)|(0.27)|
|% teachers doingmultigrade|-0.09|-0.05|-0.09|-0.06|-0.09|-0.05|
||(0.14)|(0.24)|(0.12)|(0.22)|(0.13)|(0.24)|
|**% teachers actively engaged in**<br>**teaching (mean over 4 visits)**|**0.15**|**0.18**|**0.19**|**0.20**|**0.15**|**0.18**|
||**(0.03)***|**(0.00)****|**(0.01)****|**(0.00)****|**(0.03)***|**(0.00)****|
|% teacherspresent in school|-|-|-0.09|-0.05|-|-|
||-|-|(0.19)|(0.42)|-|-|
|% contract teachers(samvida)|-|-|-|-|-0.02|0.03|
||-|-|-|-|(0.74)|(0.59)|
|% shiksha karmi teachers|-|-|-|-|0.03|0.01|
||-|-|-|-|(0.50)|(0.76)|
|Block fixed effect|Yes|Yes|Yes|Yes|Yes|Yes|
|n|2142|2142|2142|2142|2142|2142|

Robust p values in parentheses clustered at GP level, * significant at 5%; ** significant at 1%;<sup>+</sup> significant at 10%. Other controls include all student family background characteristics that are in table 8 and block fixed effects.

26
|**Table 12**|**OLS:** **Are school/teacher characteristics correlated with scores?**|
|---|---|
||**Dependent variable is percent correct score, Grades 3 and 2, MP**|

||**Hindi**<br>**Grade 3**|**Math**<br>**Grade 3**|**Hindi**<br>**Grade 3**|**Math**<br>**Grade 3**|**Hindi**<br>**Grade 2**|**Math**<br>**Grade 2**|**Hindi**<br>**Grade 2**|**Math**<br>**Grade 2**|
|---|---|---|---|---|---|---|---|---|
|**Teacher-pupil ratio**|**1.68**|**0.71**|**1.74**|**0.76**|**2.41**|**1.81**|**2.48**|**1.85**|
||**(0.15)**|**(0.41)**|**(0.15)**|**(0.40)**|**(0.06)**<sup>**+**</sup>|**(0.07)**<sup>**+**</sup>|**(0.05)***|**(0.07)**<sup>**+**</sup>|
|Index of infrastructure|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
||(0.68)|(0.93)|(0.67)|(0.94)|(0.57)|(0.61)|(0.60)|(0.60)|
|% non-sc teachers|-0.02|0.02|-0.04|0.00|0.01|0.03|0.01|0.02|
||(0.56)|(0.66)|(0.36)|(0.99)|(0.71)|(0.33)|(0.81)|(0.46)|
|% male teachers|0.02|0.02|0.00|0.01|0.03|0.04|0.03|0.04|
||(0.75)|(0.59)|(0.97)|(0.87)|(0.50)|(0.25)|(0.57)|(0.33)|
|% teachers with college<br>deg|-0.01|0.02|-0.01|0.02|0.02|0.02|0.02|0.02|
||(0.78)|(0.63)|(0.87)|(0.53)|(0.58)|(0.59)|(0.55)|(0.54)|
|% teachers with graduate<br>deg|0.12|0.10|0.13|0.11|-0.04|-0.06|-0.04|-0.06|
||(0.02)*|(0.01)*|(0.01)*|(0.01)**|(0.33)|(0.08)<sup>+</sup>|(0.38)|(0.10)<sup>+</sup>|
|Av. Years of service|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
||(0.20)|(0.51)|(0.64)|(0.94)|(0.78)|(0.75)|(0.76)|(0.44)|
|Av. days in service in<br>lastyear|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
||(0.14)|(0.32)|(0.19)|(0.41)|(0.87)|(0.98)|(0.97)|(0.89)|
|% teachers with pre<br>service training|0.02|0.00|0.02|-0.01|0.06|0.07|0.06|0.07|
||(0.59)|(0.98)|(0.69)|(0.87)|(0.09)<sup>+</sup>|(0.02)*|(0.11)|(0.03)*|
|% teachers doing multi<br>grade|-0.06|-0.10|-0.06|-0.10|-0.15|-0.12|-0.15|-0.12|
||(0.38)|(0.07)|(0.37)|(0.06)<sup>+</sup>|(0.02)*|(0.02)*|(0.02)*|(0.02)*|
|**% teachers actively**<br>**engaged inteaching**|**0.21**|**0.17**|**0.21**|**0.17**|**0.21**|**0.12**|**0.21**|**0.12**|
||**(0.01)****|**(0.00)****|**(0.00)****|**(0.00)****|**(0.00)****|**(0.02)***|**(0.00)****|**(0.03)***|
|% contract teachers<br>(samvida)|-|-|0.01|0.01|-|-|-0.03|0.00|
||-|-|(0.92)|(0.82)|-|-|(0.60)|(0.98)|
|% shiksha karmi teachers|-|-|-0.08|-0.08|-|-|-0.04|-0.04|
||-|-|(0.07)<sup>+</sup>|(0.03)*|-|-|(0.34)|(0.28)|
|Block fixed effect|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|n|2239|2239|2239|2239|2239|2239|2239|2239|

Robust p values in parentheses clustered at GP level, * significant at 5%; ** significant at 1%;<sup>+</sup> significant at 10%. Other controls include all student family background characteristics that are in table 8 and block fixed effects.

27
# **Table 13 OLS:  Are school/teacher characteristics correlated with scores?** **<u>Dependent variable is percent correct score, Grades 5 and 4, KA</u>**

||**Kannada**<br>**Class 5**|**Math**<br>**Class 5**|**Kannada**<br>**Class 5**|**Math**<br>**Class 5**|**Kannada**<br>**Grade 4**|**Math**<br>**Grade 4**|**Kannada**<br>**Grade 4**|**Math**<br>**Grade 4**|
|---|---|---|---|---|---|---|---|---|
|**Teacher-pupil ratio**|**-0.03**|**0.12**|**-0.10**|**0.06**|**-0.18**|**0.48**|**-0.20**|**0.46**|
||**(0.93)**|**(0.75)**|**(0.82)**|**(0.87)**|**(0.71)**|**(0.27)**|**(0.68)**|**(0.30)**|
|Index of infrastructure|0.01|0.00|0.01|0.00|0.00|-0.01|0.00|-0.01|
||(0.01)*|(0.46)|(0.03)*|(0.68)|(0.51)|(0.10)|(0.75)|(0.04)*|
|% non-sc teachers|-0.01|0.03|-0.01|0.04|0.01|0.01|0.02|0.02|
||(0.68)|(0.39)|(0.78)|(0.33)|(0.80)|(0.85)|(0.68)|(0.71)|
|% male teachers|0.03|0.07|0.01|0.06|-0.04|0.01|-0.05|-0.01|
||(0.46)|(0.06)|(0.73)|(0.11)|(0.29)|(0.85)|(0.15)|(0.77)|
|% teachers with college<br>deg|0.02|-0.02|0.02|-0.02|-0.06|-0.06|-0.06|-0.06|
||(0.67)|(0.42)|(0.57)|(0.50)|(0.14)|(0.13)|(0.15)|(0.14)|
|% teachers with<br>graduate deg|0.20|0.09|0.21|0.10|0.36|0.20|0.38|0.22|
||(0.07)|(0.43)|(0.05)*|(0.35)|(0.00)**|(0.07)|(0.00)**|(0.03)*|
|Av. Years of service|0.00|0.00|0.00|-0.004|0.00|0.00|0.00|0.00|
||(0.27)|(0.06)|(0.24)|(0.05)|(0.45)|(0.75)|(0.55)|(0.62)|
|Av. days in service in<br>lastyear|-0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
||(0.71)|(0.72)|(0.72)|(0.66)|(0.37)|(0.44)|(0.35)|(0.43)|
|% teachers with pre<br>service training|0.03|0.03|0.03|0.04|-0.05|0.02|-0.05|0.02|
||(0.57)|(0.38)|(0.51)|(0.33)|(0.35)|(0.75)|(0.32)|(0.77)|
|% teachers doing multi<br>grade|-0.05|-0.04|-0.05|-0.04|0.04|0.05|0.04|0.05|
||(0.31)|(0.27)|(0.32)|(0.29)|(0.41)|(0.34)|(0.41)|(0.35)|
|**% teachers actively**<br>**engaged inteaching**|**0.02**|**0.01**|**-0.12**|**-0.13**|**0.06**|**0.09**|**-0.08**|**-0.06**|
||**(0.77)**|**(0.89)**|**(0.16)**|**(0.10)**|**(0.39)**|**(0.18)**|**(0.34)**|**(0.49)**|
|**% teachers present in**<br>**school (mean over 4**<br>**visits)**|**-**|**-**|**0.26**|**0.27**|**-**|**-**|**0.28**|**0.31**|
||**-**|**-**|**(0.02)***|**(0.02)***|**-**|**-**|**(0.01)****|**(0.01)****|
|Block fixed effect|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|n|2086|2086|2086|2086|2103|2103|2103|2103|

Robust p values in parentheses clustered at GP level, * significant at 5%; ** significant at 1%. Other controls include all student family background characteristics that are in table 8 and block fixed effects.

28
# **<u>Table 14 Average teacher attendance and activity</u>**

||**UP**|**Attendance**<br>**MP**|<br>**KA**|<br>**UP**|**Activity**<br>**MP**|**KA**|
|---|---|---|---|---|---|---|
|All|0.64|0.67|.88|0.25|0.3|.68|
|Regularteachers|0.61|0.64|.88|0.19|0.25|.68|
|Contract|0.74|0.72|-|0.36|0.36|-|
|Former contract|-|0.71|-|-|0.34|-|

# **<u>Table 15 Average teacher characteristics</u>**

|Percent unless indicated<br>otherwise|<br>Regular|**UP**<br>Contract|Regular<br>|**MP**<br>Contract<br>|Former<br> contract|<br>**KA**<br>All|
|---|---|---|---|---|---|---|
|Age (years)|46|27|45|31|34|.39|
|Non-SC/ST|.83|.76|.75|.65|.65|.76|
|Male|.71|.45|.93|.72|.70|.59|
|Highest education- whether<br>class12|.49|.32|.58|.42|.38|.72|
|whethercollege|.24|.45|.25|.39|.33|.26|
|whetherGraduate|.28|.24|.17|.18|.29|.03|
|Pre service training|.93|.08|.56|.16|.22|.93|
|Days of in service training in<br>last year|5|6|10|11|12|4|
|Years ofteaching experience|17|2|21|5|9|12|
|Distance commute to school<br>(in km)|10|1|9|11|8|11|
|Whether teach multigrades|.76|.89|.86|.90|.86|.91|

29
**Table 16 OLS: Village fixed effects regression of teacher effort, UP** (Dependent variable has four observations per teacher: Attendance=1 if teacher present, 0 otherwise: Activity=1 if teacher actively <u>engaged in teaching, 0 otherwise)</u>

||**Attendance**|**Attendance**|**Activity**|**Activity**|
|---|---|---|---|---|
|**Whether teacher is contract**<br>**teacher (****_shiksha mitra_) **|**0.12**|**0.10**|**0.07**|**0.07**|
||**(0.00)****|**(0.05)**<sup>*****</sup>|**(0.01)****|**(0.04)***|
|Age|-0.003|-0.004|-0.004|-0.004|
||(0.12)|(0.11)|(0.00)**|(0.05)*|
|Caste (1 if non-SC)|-0.03|-0.03|0.01|0.01|
||(0.34)|(0.31)|(0.65)|(0.63)|
|Gender(1 if male)|-0.01|-0.02|-0.09|-0.09|
||(0.70)|(0.57)|(0.00)**|(0.00)**|
|College educated|-0.08|-0.07|-0.04|-0.04|
||(0.02)*|(0.04)*|(0.12)|(0.13)|
|Graduate degree|-0.15|-0.13|-0.07|-0.07|
||(0.00)**|(0.00)**|(0.01)**|(0.01)*|
|Pre service training||-0.04||-0.01|
|||(0.39)||(0.88)|
|Years ofservice||0.00||0.00|
|||(0.22)||(0.69)|
|Days of inservice training||0.00||0.00|
|||(0.90)||(0.63)|
|Village/school fixed effect<br>(200 villages)|YES|YES|YES|YES|
|R<sup>2</sup>|.17|.18|.23|.23|
|Observations|2417|2416|2416|2415|
|Robust p valuesinparentheses c|lustered at teach|er level|||
|*significant at 5%;**significa|nt at1%||||

30
**Table 17 OLS: Village fixed effects regression of teacher effort, MP** (Dependent variable has four observations per teacher: Attendance=1 if teacher present, 0 otherwise: Activity=1 if teacher actively <u>engaged in teaching, 0 otherwise)</u>

||**Attendance**|**Attendance**|**Activity**|**Activity**|
|---|---|---|---|---|
|**Whether****_samvida shikshak_**<br>**(contract teacher)**|**0.06**|**0.08**|**0.07**|**0.10**|
||**(0.18)**|**(0.14)**|**(0.08)**|**(0.03)***|
|**Whether****_shiksha karmi_**<br>**(former contract teachers)**|**0.02**|**0.02**|**0.03**|**0.05**|
||**(0.66)**|**(0.62)**|**(0.32)**|**(0.16)**|
|Age|-0.01|-0.01|-0.01|-0.01|
||(0.00)**|(0.04)*|(0.01)**|(0.01)**|
|Caste (1 if non-SC/ST)|-0.01|-0.01|0.02|0.02|
||(0.61)|(0.66)|(0.18)|(0.16)|
|Gender(1 if male)|0.05|0.05|-0.01|-0.01|
||(0.15)|(0.15)|(0.81)|(0.66)|
|College educated|-0.05|-0.05|-0.06|-0.06|
||(0.08)|(0.14)|(0.04)*|(0.05)*|
|Graduate degree|-0.03|-0.03|-0.03|-0.04|
||(0.41)|(0.45)|(0.30)|(0.23)|
|Pre service training|-|-0.03|-|-0.03|
|||(0.35)||(0.38)|
|Years ofservice|-|0.00|-|0.01|
|||(0.68)||(0.04)*|
|Days of inservice training|-|0.00|-|0.00|
|||(0.55)||(0.23)|
|Village fixed effect (198<br>villages)|YES|YES|YES|YES|
|R<sup>2</sup>|.23|.23|.22|.22|
|Observations|1800|1792|1800|1792|
|Robust p valuesinparentheses|clustered at teac|her level|||
|* significant at5%;** significa|nt at 1%||||

31
**Table 18 OLS: Village fixed effects regression of teacher effort, KA** (Dependent variable has four observations per teacher: Attendance=1 if teacher present, 0 otherwise: Activity=1 if teacher actively <u>engaged in teaching, 0 otherwise)</u>

||**Attendance**|**Attendance**|**Activity**|**Activity**|
|---|---|---|---|---|
|Age|0.00|0.00|0.003|0.004|
||(0.53)|(0.13)|(0.03)*|(0.05)*|
|Caste (1 if non-SC/ST)|0.00|-0.01|-0.01|-0.01|
||(0.68)|(0.60)|(0.32)|(0.27)|
|Gender(1 if male)|-0.04|-0.04|-0.06|-0.06|
||(0.01)**|(0.01)**|(0.01)*|(0.01)*|
|College degree|0.03|0.03|0.07|0.07|
||(0.11)|(0.10)|(0.01)**|(0.01)**|
|Graduate degree|-0.03|-0.04|-0.14|-0.15|
||(0.52)|(0.39)|(0.04)*|(0.03)*|
|Pre service training|-|-0.01|-|0.02|
|||(0.84)||(0.61)|
|Years ofservice|-|0.00|-|0.00|
|||(0.22)||(0.50)|
|Days of inservice training|-|0.002|-|0.00|
|||(0.03)*||(0.30)|
|Village/school fixed effect<br>(210 villages)|YES|YES|YES|YES|
|R<sup>2</sup>|.15|.15|.14|.14|
|Observations|3374|3374|3374|3374|
|Robust p valuesinparentheses|clustered at teac|her level|||
|*significant at 5%;**signific|ant at1%||||

32
|**Table 19**<br>**VEC members’ knowledge and par**|**ticipation, UP**|||
|---|---|---|---|
|Percentage members that say yes orgive the correct response|Headmaster|_Pradhan_|Parents|
|Doyou know ifyou are a member of the VEC|91|86|85|
|Have you received any training on roles/responsibilities of<br>VEC|11|3|2|
|Has VEC met in this schoolyear|78|49|30|
|Has VEC inspected the school in this schoolyear|62|62|38|
|Doyou know who operates the school account|78|68|28|
|Doyou know which account scholarshipcomes to|75|57|28|
|Doyou know which account mid daymeal moneycomes to|67|54|17|
|How many other VEC members can you name (on average<br>VEChas 5members): **no of members named correctly**|3|2|1|
|Do you know how much money came into the school<br>accountinthis schoolyear|82|56|9|
|Do you know how much money remains in the school<br>account at present|74|25|5|
|Do you know if VEC checks/sees the attendance of_shiksha_<br>_mitra_ everymonth|73|65|35|
|Do you know what can VEC do if_shiksha mitra_is irregular<br>||||
|or teaches unsatisfactorily|55|45|21|
|<br>**Table 20**<br>**Can VEC members list their roles/**<br>|**responsibilitie**|**s?, UP**||
|Percentage members that list each item|Headmaster|_Pradhan_|Parents|
|Inspect schools|49|38|21|
|Manage civil works|43|16|12|
|Prepare schemes/plans for school improvement|25|13|7|
|Manage school accounts, decide how to spend funds in<br>schoolaccounts|9|6|2|
|Select shiksha mitra|5|5|2|
|Ensure teachers come regularlyand on time|37|29|15|
|Ensure teachers teach satisfactorily|42|27|21|
|Complain to higher authorities if teachers<br>performance/attendanceis unsatisfactory|21|21|11|
|Ensure distribution of textbooks|14|11|4|
|Ensure distribution of scholarships|24|16|11|
|Ensurepreparation and distribution ofqualitymid daymeals|34|35|26|
|Ensure distribution of uniforms|10|10|6|

33
# **<u>Table 21 PTA members’ knowledge and participation, MP</u>**

|Percentage members that say yes orgive the correct response|Headmaster|PTAchair|Parents|
|---|---|---|---|
|Do you know if you are a member of the PTA executive<br>committee|98|97|70|
|Have you received any training on roles/responsibilities of<br>PTA|80|42|8|
|Has PTA met in this schoolyear|98|92|55|
|Has PTA inspected the school in this schoolyear|89|87|34|
|Doyou know who operates the school account|93|73|19|
|Doyou know which account scholarshipcomes to|74|18|8|
|Doyou know which account mid daymeal moneycomes to|94|74|24|
|How many other PTA members can you name (on average<br>PTA has10-13members): **no of members named correctly**|5|4|1|
|Do you know how much money came into the school account<br>inthis schoolyear|93|49|2|
|Do you know how much money remains in the school account<br>at present|86|27|1|
|Do you know if PTA chair checks/sees the attendance of|68|72|29|
|teachers everymonth||||
|**Table 22**<br>**Can PTA members list their roles/**|**responsibiliti**|**es?, MP**||
|Percentage members that list each item|Headmaster|PTAchair|Parents|
|Inspect schools|59|52|16|
|Manage civil works|46|21|5|
|Prepare schemes/plans for school improvement|26|5|2|
|Manage school accounts, decide how to spend funds in<br>schoolaccounts|17|4|1|
|Verify and sign on teachers salary slip/attendance register<br>each month|14|6|1|
|Ensure teachers come regularlyand on time|42|47|20|
|Ensure teachers teach satisfactorily|35|49|21|
|Recommend reducing/stopping teachers’ salary if teachers<br>performance/attendanceis unsatisfactory|6|3|1|
|Ensure distribution of textbooks|18|5|1|
|Ensure distribution of scholarships|15|4|1|
|Ensurepreparation and distribution ofqualitymid daymeals|73|70|28|
|Ensure distribution of uniforms|23|12|3|

34
# **<u>Table 23 SDMC members’ knowledge and participation, KA</u>**

|Percentage members that say yes or give the correct response|Headmaster|SDMC<br>Chair|Parent/other<br>members|
|---|---|---|---|
|Doyou know ifyou are a member of the SDMC|88|92|96|
|Have you received any training on roles/responsibilities of<br>SDMC|45|31|20|
|Has SDMC met in this schoolyear|89|81|69|
|Has SDMC inspected the school in this schoolyear|82|80|63|
|How many other SDMC members can you name (on average<br>SDMC has 10 members).<br>**No of members named correctly**→|6|5|4|
|Doyou know who operates the school account|79|76|67|
|Doyou know which account scholarshipcomes to|88|77|67|
|Doyou know which account mid daymeal moneycomes to|92|92|95|
|Do you know how much money came into the school<br>accountinthis schoolyear|63|47|15|
|Do you know how much money remains in the school<br>account at present|74|51|14|

|**Table 24**<br>**Can SDMC members list their rol**<br>|**es/responsibili**<br>|**ties?, KA**<br>||
|---|---|---|---|
|Percentage members that list each item|Headmaster|Chair|Parents|
|Inspect schools|79|79|62|
|Manage civil works|75|68|49|
|Prepare schemes/plans for school improvement|63|58|41|
|Manage school accounts, decide how to spend funds in<br>schoolaccounts|48|46|31|
|Ensure teachers come regularlyand on time|50|51|30|
|Ensure teachers teach satisfactorily|34|41|28|
|Complain to higher authorities if teachers<br>performance/attendanceis unsatisfactory|27|35|27|
|Ensure distribution of textbooks|42|46|33|
|Ensure distribution of scholarships|37|37|33|
|Ensurepreparation and distribution ofqualitymid daymeals|46|45|43|
|Ensure distribution of uniforms|39|33|25|

35
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