THE ACHIEVEMENT GAP BETWEEN GOVERNMENT and PRIVATE SCHOOLS in PAKISTAN a Thesis Submitted to the Faculty of the Graduate Scho

THE ACHIEVEMENT GAP BETWEEN GOVERNMENT AND PRIVATE SCHOOLS IN PAKISTAN

A Thesis submitted to the Faculty of the Graduate School of Arts and Sciences of Georgetown University in partial fulfillment of the requirements for the degree of Master of Public Policy in Public Policy

Maryam Akmal, B.A.

Washington, DC April 12, 2016

THE ACHIEVEMENT GAP BETWEEN GOVERNMENT AND PRIVATE SCHOOLS IN PAKISTAN

Maryam Akmal, B.A.

Thesis Advisor: Adam Thomas, Ph.D.

ABSTRACT Learning outcomes in Pakistan have traditionally been poor. However, over the last two decades, the educational market place has changed substantially. In particular, enrollment in private schools has increased dramatically across a broad range of income groups in both urban and rural areas. Given the important role of private schools in Pakistan's educational landscape, there is an increasing focus on the learning gap between government and private schools. Using household-level data from rural and urban areas of Pakistan, this study estimates the extent to which private school students perform better than government school students.

iii

I am grateful to Professor Adam Thomas for his guidance, support and encouragement throughout this project.

Many thanks, Maryam Akmal

TABLE OF CONTENTS Introduction ...... 1 Background ...... 3 Literature Review ...... 4 Conceptual Framework and Hypothesis ...... 8 Data and Methods ...... 11 Descriptive Statistics ...... 15 Results ...... 20 Discussion ...... 30 References ...... 34 Appendix ...... 38

LIST OF TABLES Table 1: Definitions of Variables ...... 14 Table 2: Descriptive Statistics for Dependent, Key Independent and Control Variables ...... 17 Table 3: Key Characteristics Disaggregated by School Type ...... 19 Table 4: Regression Results for Math Test Scores ...... 21 Table 5: Regression Results for English Reading Scores ...... 24 Table 6: Regression Results for Local Reading Scores ...... 27

INTRODUCTION Pakistan has the second highest number of out-of-school children in the world (UNESCO,

2015).1 Even among children who are enrolled in school, learning levels are low. Estimates suggest that only 43 percent of grade-five students can perform grade-two level division, while only 50 percent of students in grade five can read grade-two level sentences in Pakistan's national language, Urdu (ASER Pakistan, 2013). Based on the current state of education, even if all Pakistani children were enrolled in school, many of the students would still be functionally illiterate and innumerate (Das et al., 2006).

In order to institute effective policy reform in Pakistan's education sector, it is important to identify key factors contributing to the poor learning inside the classrooms. The case for focusing on student learning may be strengthened even further by the possible link between quality of education and enrollment; improved learning in schools may boost enrollment and retention, as parents and children gain a higher return on their investment of time and resources.

Education is a key driver of individual earnings and national economic growth (Hanushek and Wößmann, 2007). According to the Pakistan Bureau of Statistics (PBS), more than one-third of Pakistan's population is below the age of ten. Many policymakers believe that sustained national growth will require that this significant segment of the population receives a high- quality education. The government of Pakistan has demonstrated its commitment to improving education access and quality by making education a key priority of Pakistan's National Plan of

Action (2013). Pakistan’s education budget, which has averaged at around 2 percent of GDP in

1 According to UNESCO’s most recent estimates, 6.7 million Pakistani children are out of school, of which more than half are female. 1

recent years, is primarily used for teacher salaries. According to the World Bank (2008), 90 percent of Pakistan's education budget is spent on the salaries of approximately 1.5 million teachers. However, notwithstanding this investment in improving the quality of classroom instruction, student learning outcomes continue to be disappointing.

Numerous studies have explored the impact of various educational inputs, such as teachers, facilities and curricula, on student learning. Hanushek (2003) criticizes the emphasis on "input-based" rather than "incentive-based" education policies, arguing that increasing resources does not significantly improve student learning. In Pakistan, government and private schools have different incentive structures. For example, teachers in government schools tend to be paid better and have more training and experience than private school teachers.

However, salaries of teachers in government schools are usually tied to education and seniority rather than to student learning outcomes (Andrabi et al., 2010). Keeping in mind the different incentive structures, this study analyzes education outcomes in government and private schools across Pakistan using survey data from the Annual Status of Education Report (ASER) 2014. The survey covers 144 districts of Pakistan and provides information about student learning, family characteristics and household information.2

The remainder of this paper is organized as follows. In the next section, I provide background on the evolution of private schooling in Pakistan. I then review the relevant literature and describe my conceptual framework, data, econometric methods and descriptive statistics. Lastly, I discuss my findings and results.

2 ASER selects districts based on the presence of local collaborating partners (ASER, 2010). While at present only 144 districts are surveyed, the ultimate goal of ASER is to cover all 157 districts of Pakistan. 2

BACKGROUND

Approximately one-third of all students in Pakistan attend private schools (Nguyen and

Raju, 2014). These schools serve a range of income groups and are prevalent in both rural and urban areas (Andrabi et al., 2002). While there are elite private schools catering to high-income groups in Pakistan, this paper focuses on the majority of private schools that are low-cost enterprises serving low- and middle-income communities. These businesses are largely unregulated and receive almost no government support (Andrabi et al., 2010). They tend to have low operational costs and are often run out of the owners' homes (Andrabi et al., 2010).

Private schools also employ more untrained staff than government schools, where teachers are paid significantly more (Andrabi et al. 2010; French and Kingdon 2010).

According to Andrabi et al. (2006), in many developing countries, the per-child costs in private schools are significantly lower than the per-child costs in government schools due to the pay scales for government teacher salaries. Private schools tend to employ teachers with lower academic qualifications and training. As a result, based on qualifications alone, teacher quality seems lower in the private sector. However, teacher effectiveness is a function of both qualifications and motivation, the latter of which is hard to measure. It is possible that the different incentive structures in government and private schools may influence teachers' effort and motivation, and as a result, the quality of learning (French and Kingdon, 2010). Despite their lower costs and inferior hiring practices, private schools are largely perceived as providing better quality education than their government counterparts, as evidenced by the rising demand for private education in Pakistan.

The number of private schools in Pakistan more than doubled from 30,000 in the 1990s to 70,000 in 2008 (Nguyen and Raju, 2014).3 In the last two decades, enrollment in private schools has increased across a broad range of income groups, including high-income, urban households and low-income, rural ones (Ibid.). Using LEAPS (Learning and Educational

Achievements in Punjab Schools) data collected between 2004 and 2007 in three districts of

Punjab, Andrabi et al. (2010) conclude that average student achievement is significantly higher in private schools than in government schools. Such findings corroborate the current perception of superior educational quality in private schools.

LITERATURE REVIEW

Evidence for poor learning outcomes across schools in Pakistan is well-documented.

Poor student performance is observed across all types of schools, including government and private schools (Andrabi et al. 2007; ASER 2013). However, fewer studies contribute directly to the growing debate about the difference in education outcomes between public and private schools in Pakistan. While there has been increasing recognition of the potential for low-cost private schools to improve education access and quality in Pakistan, most existing studies are of restricted geographical scope due to limited data.

Demand for Private Schools in Pakistan

There is substantial evidence documenting the rise in demand for private education in

Pakistan since the 1980s. Andrabi et al. (2005) find evidence of significant growth in the number of private schools in rural areas, thus dispelling earlier assertions by Jimenez and Tan (1987)

3 Enrollment in private schools increased from less than 5 percent in 1990 to 35 percent in 2005 (Andrabi et al., 2010). In 1999 alone, 8000 new private schools were created. Roughly half of these private schools were in rural areas (Andrabi et al., 2006). 4

that private schools are an urban phenomenon and cater to a relatively wealthy clientele (Arif and Saqib 2003; Nguyen and Raju 2014). On the contrary, Andrabi et al. (2002) find that most

Pakistani private schools are catering to low- and middle-income populations in rural areas, rather than to the elite. Muralidharan and Kremer (2006) observe a similar trend in India, where private schools are widespread in rural areas. Furthermore, Andrabi et al. (2010) find that the cost of educating a Pakistani child is 40 percent lower in private schools than in government schools. Alderman et al. (2001) also find evidence of high demand for private education among low-income households in Pakistan. Their study shows that many poor households use private schools even when faced with higher tuition costs, due to parents' preference for the perceived higher quality of instruction in private schools.

Learning Outcomes in Government and Private Schools in Pakistan

Several studies find that private schools positively affect learning outcomes. The strongest study on learning gaps between government and private schools is by Andrabi et al.

(2010) who instrument for private school enrollment using the distance to a private school relative to the distance to a government school. They find a significant relationship between private school attendance and learning outcomes in three districts of rural Punjab in Pakistan.

Their study reveals that for children with similar characteristics test scores are 0.8 to 1 standard deviations higher in government schools than in private schools. In addition, private school students have higher scores on tests of civic values that measure understanding of concepts such as democracy and gender equality. While the study is limited in geographical scope (as it is confined to three districts in rural Punjab), it presents the most credible evidence to date in support of the claim that private schools produce better learning outcomes.

Das et al. (2006), using data from the Punjab province of Pakistan, find evidence of poor academic outcomes in both government and private schools. The authors find that at least 50 percent of the variation in learning outcomes can be accounted for by differences in school type, such as government versus private schools, or "bad" government schools versus "good" government schools. For example, the gap in English test scores between students from government and private schools is twelve times larger than the gap between children from rich and poor families. In another analysis that uses household survey data from low-income areas of Lahore City, Alderman et al. (2001) find that children in private schools outperform government school students after controlling for family characteristics and school inputs. Using the nationwide ASER Pakistan 2011 data, Amjad and MacLeod (2011) find that the quality of education is poor across government schools, private schools and schools with public-private partnerships. In addition, they find that students from private schools outperform students from government schools.

Aslam (2009) uses a Heckman two-step procedure to overcome the possibility of sample selection bias among children who go to public and private schools in Lahore City. Sample selection bias could arise if parents sending children to private schools have a greater interest in their child's academic success, which could affect the student's achievement regardless of the type of school attended. The author's Heckman-corrected results are not significantly different from her Ordinary Least Squares (OLS) estimates. These results corroborate earlier findings by Andrabi et al. (2010), which show that private schools are more effective at teaching math and literacy skills than government schools.

Not all evidence supports the notion that differences in student performance can be attributed to school type. Arif and Saqib (2003) sample 50 schools across the country and find evidence of better learning outcomes among private school students, but they also find that the difference can largely be explained by family background and school characteristics, such as teacher qualification and student-teacher ratio. Performance also varies by district, with government schools performing better than private schools in some districts. In addition, the authors argue that low-income households tend to send their children to government schools, as private education is largely unaffordable for them.

Learning Outcomes in Government and Private Schools in Other Developing Countries

Studies comparing outcomes across government and private schools in other developing countries largely provide support for the claim that students have better learning outcomes in private schools. French and Kingdon (2010) examine ASER data from India to estimate the effect of private school enrollment on learning outcomes. They use a variety of techniques to estimate the effect of private schools, such as OLS estimation, cross-section fixed effect techniques at the level of state, district, village and households, and panel data analysis using village and time fixed effects. The authors find that there is a private school effect on child achievement of 0.17 standard deviations. Another study by Muralidharan and Kremer (2006) in

India finds evidence of a sizeable and significant association between private school attendance and learning outcomes after controlling for family and household characteristics.

A descriptive study by Tooley and Dixon (2005) finds that private schools in India,

Ghana, Nigeria and Kenya are popular among low-income segments, have better achievement scores and have lower teacher costs. Jimenez et al. (1991) use data from Colombia, the

Dominican Republic, the Philippines, Tanzania and Thailand, to show that private school students perform better on standardized math and English tests than government school students, even after controlling for income. In addition, they find evidence that private schools have lower per-unit costs than government schools, substantiating claims of lower private school costs in Pakistan by Andrabi et al. (2010).

The Present Study

Many studies have examined the learning gap between government and private schools in various developing countries. However, only a few have focused on learning outcomes in government and private schools in Pakistan. To date, the most informative studies from

Pakistan have utilized data from a restricted geographical area (Andrabi et al. 2006; Das et al.

2006). This study uses ASER survey data from 2014, covering most regions of Pakistan, to present a comprehensive nation-wide assessment of learning in government and private schools.

CONCEPTUAL FRAMEWORK AND HYPOTHESIS

Based on the findings in the existing literature, I hypothesize that attending private schools is positively correlated with learning outcomes. In other words, I predict that there is a significant learning gap between students attending private schools and students attending government schools. In addition, I expect the size of the private school advantage to vary by district (Das et al. 2006). While private schools have grown in both rural and urban districts, their effects may vary due to the different social and economic characteristics of the districts.

While the purpose of this study is to investigate the effect of school type (government versus private) on learning levels, many other factors also contribute to the difference in achievement among students. These factors, which are related to both learning levels and school type, can be broadly categorized into individual child characteristics, parent characteristics and household characteristics. In order to isolate the relationship between school type on learning outcomes, it is important to control for these factors. This study uses control variables from the nationally representative ASER Pakistan survey, which provides child-

, parent- and household-level data across rural and urban districts of Pakistan.

Child Characteristics

Apart from the well-documented link between a child's natural cognitive ability and learning outcomes, other child characteristics, such as number of siblings, gender and access to tutoring outside of school are also related to educational achievement. Numerous studies show an inverse relationship between family size and learning outcomes (Alwin 1991; Shavit and

Pierce 1991; Downey 1995). One possible theory explaining this relationship is the "resource dilution effect": as parent's finite resources, such as income and time, are spread out among more siblings, the lower resources per child are related to lower education outcomes (Downey,

2001). Given the strong gender bias in enrollment and learning in Pakistan, parents may choose to spend more on a male child's education.4 For example, Aslam (2009) finds that boys in

Pakistan are more likely to be enrolled in comparatively high-quality private schools than girls.

4 According to a 2014 report by the World Economic Forum, Pakistan ranks 132 out of 142 countries in terms of the gender gap in educational attainment. Another study by the Brookings Institution (King and Winthrop, 2015) finds that there are 74 girls for every 100 boys enrolled in secondary school in Pakistan. 9

Private tutoring outside of school is prevalent among students from both rural and urban areas (Dundar et al., 2014). Aslam (2009) posits that part of the reason for the uptake of private tutoring could be the poor quality of education provided in schools, with quality being worse in government schools. The probability of receiving extra tutoring is also related to income, gender and maternal education (Macpherson et al., 2014).

Household Characteristics

Many studies from high-income countries have documented the positive relationship between income and test scores (Dahl and Lochner 2012; Duncan and Magnuson 2005; Davis-

Kean 2005). Das et al. (2006) find a similar relationship between parental income and child's educational achievement in Pakistan. However, the authors find that the achievement gap attributable to parents' income and education is significantly reduced once one accounts for differences in school type. Income also affects availability of electricity, water and other items that facilitate a productive learning environment at home. Furthermore, income and transportation availability determine whether schools are physically accessible. In Pakistan, where female mobility is restricted due to cultural expectations, distance from school affects girls more than boys (Alderman et al., 2001). Andrabi et al. (2006) find that private schools help to mitigate the negative effect of distance on school attendance and boost overall enrollment for both boys and girls.

Parent Characteristics

There is ample evidence documenting a positive relationship between parents' level of education and children's learning outcomes (Davis-Kean 2005; Dubow 2010). In Pakistan,

Andrabi et al. (2009) find that the test scores of children whose mothers have some education

are 0.24 to 0.35 standard deviations higher than children with uneducated mothers.

Furthermore, mothers with some education are more likely to spend time helping children with homework (Ibid.). It is possible that educated parents are also more likely to choose between public and private schools based on perceived quality.

DATA AND METHODS

My empirical analyses use data from the Annual Status of Education Report (ASER)

Pakistan 2014 survey covering children aged 3 to 16 years in 123 rural districts and 21 urban centers of Pakistan. ASER assessment tools test children's learning through a grade one to two level test on arithmetic, English and reading (Urdu, Sindhi or Pashto). Each student is assigned a learning level between one (lowest) to five (highest). The goal of the assessment is to obtain information on the basic reading and arithmetic abilities of children. For the purposes of this study, I recode the test score variables into dichotomous variables taking the value of one if students score above the average for the subject and zero if students score below the average.

This approach allows me to produce more easily interpretable regression results.

Since the ASER test is only administered to children above five years of age, I exclude all children who are below five years of age from my analysis. Furthermore, I exclude any children who went to religious or other types of schools from the sample, as this study is focused only on differences in learning outcomes between students enrolled in government and private schools.5

5 Religious schools or madrassahs, unlike public and private schools, primarily teach religious subjects. However, some madrassahs also teach non-religious subjects such as math in combination with religious studies. 11

I control for variables that are plausibly related to both learning outcomes (the dependent variable) and the probability of going to private schools over government schools

(the key independent variable). As is implied in the discussion in the previous section these control variables can be categorized into three broad categories: child characteristics, household characteristics and parent characteristics (See Table 1).

To analyze the relationship between school type (a dummy variable that takes on a value of one if the child goes to a private school and zero otherwise) and learning outcomes (a dummy variable that takes on a value of one if the child scores above the average and zero otherwise), I estimate a Linear Probability Model (LPM) with district-level fixed effects. The LPM allows me to predict the change in probability of scoring above the average given a change in school type from government to private. My fixed effects specification controls for time- invariant characteristics of the individual districts – for example, unobserved administrative differences between districts that could affect resources allocated to schools. This approach is consistent with that of Aslam (2009), who estimated a village-level fixed-effects model to study the relative effectiveness of school types in Lahore, Pakistan. I subsequently apply more stringent fixed effects controls at the village and sibling level.

I cannot use a simple OLS model to estimate the effect of private schooling on learning outcomes due to sample selection bias. It is possible that the choice of private schools is related to unobserved characteristics of the child and the family that are also related to achievement scores. As suggested by French and Kingdon (2010), OLS estimates provide an upper bound of the private school effect, as they include the effect of unobservable factors in addition to the private school effect. To apply stricter controls, I use district-level fixed effects.

A simple version of my regression equation is as follow:

LearningLevelid = β0 + β1Privateid + βxxXXid+ αd + uid where XX represents all the control variables listed in Table 1. The subscripts "i" and "d" represent each individual child and district respectively.

I estimate three separate regression equations, with each equation using one of three dependent variables measuring learning outcomes: math, English and reading in the local language.

Table 1: Definitions of Variables

Dependent Variable

Math A dichotomous variable indicating whether the child scored above Highest the average in math (0 = no, 1 = yes).6 Level

English A dichotomous variable indicating whether the child scored above Learning Level Reading the average in English (0 = no, 1 = yes).7

A dichotomous variable indicating whether the child scored above Local the average in local language: Urdu, Sindhi, Pashto (0 = no, 1 = Reading yes).8 Independent Variable A dichotomous variable indicating the type of school attended by Private the child (0 = government, 1 = private). Child Characteristics A dichotomous variable indicating the child’s gender (0 = male, 1 = Female female). A continuous variable indicating the child’s age (between 5-16 Age years). Grade A continuous variable indicating the grade of the child: 0-12. A dichotomous variable indicating whether the child takes paid Child Taking Paid Tutoring tutoring outside of school (0 = no, 1 = yes). A continuous variable indicating the school's tuition fee in Pakistani Tuition Fee9 Rupees. Household Characteristics A continuous variable indicating the total number of members in Household Size the surveyed household. A dichotomous variable indicating that the construction type of House Type Mixed house is mixed (0 = not mixed, 1 = mixed). House Type Solid A dichotomous variable indicating that the construction type of

6 The average score for math is 3.450 on a scale of 1 to 5 (1 = beginner/nothing, 2 = recognition of 1-9, 3 = recognition of 10-99, 4 = subtraction, 5 = division). 7 The average score for English reading is 3.468 on a scale of 1 to 5 (1 = beginner/nothing, 2 = capital letters, 3 = small letters, 4 = words, 5 = sentences). 8 The average score for local language reading is 3.476 on a scale of 1 to 5 (1 = beginner/nothing, 2 = letters, 3 = words, 4 = sentences, 5 = story). 9 Government schools in Pakistan tend to charge a small tuition fee (Kattan & Burnett, 2004). For the government schools sampled in ASER 2014 data, tuition fees range from Rs. 20 ($0.19) to Rs. 8000 ($75.88) per month. 14

house is solid (0 = not solid, 1 = solid). A dichotomous variable indicating whether the house is owned by House Owned the residents (0 = no, 1 = yes). Electricity Connection A dichotomous variable indicating whether the house has an Available electrical connection (0 = no, 1 = yes). A dichotomous variable indicating whether there is a mobile phone Mobile Available available for use by household members (0 = no, 1 = yes). A dichotomous variable indicating whether there is a television in TV Available the household (0 = no, 1 = yes). A continuous variable indicating the one-way distance from the Nearest School Distance house to the nearest school in kilometers. Parent Characteristics Mother's Age A continuous variable indicating the age of the child's mother. Mother's Education A dichotomous variable indicating whether the child's mother has Secondary completed secondary school (0 = no, 1 = yes). Mother's Education High A dichotomous variable indicating whether the child's mother has School completed high school (0 = no, 1 = yes). Mother's Education Some A dichotomous variable indicating whether the child's mother has College completed some college (0 = no, 1 = yes). Father's Age A continuous variable indicating the age of the child's father. Father's Education A dichotomous variable indicating whether the child's father has Secondary completed secondary school (0 = no, 1 = yes). Father's Education High A dichotomous variable indicating whether the child's father has School completed high school (0 = no, 1 = yes). Father's Education Some A dichotomous variable indicating whether the child's father has College completed some college (0 = no, 1 = yes).

DESCRIPTIVE STATISTICS

Table 2 provides descriptive statistics for the dependent and key independent variables, and for child, household and parent characteristics. Appendix A supplements Table 2 by providing descriptive statistics by region type (urban versus rural). Table 3 presents differences in key characteristics by school type (government versus private).10 11

10 All descriptive statistics are unweighted. According to ASER survey documentation, weighted estimates are the same as unweighted estimates at the district level due to the use of probability-based sampling (PBS). 15

As shown in Table 2, average learning levels among students in both government and private schools are low. The mean value for all three learning outcome variables is approximately 0.52, implying that most students' ASER test scores are below the basic proficiency levels expected in grade one to two. Furthermore, the mean and standard deviations for student outcomes are similar across math, reading in English and reading in local language, which shows that students tend to do poorly across all tests. In line with the national gap in male-female literacy rates, fathers tend to be more educated than mothers.12 The average tuition fee for government and private schools is Rs. 359 ($3.42) per month. However, there is substantial variation in tuition fees, ranging from Rs. 20 ($0.19) per month to Rs. 8,000

($75.88) per month.13 The maximum household size is 74, implying that some families in the sample reside in shared households.14

11 The original sample had 238,843 observations. Missing values for tuition fee were derived using regression mean imputation. In the original sample, 213,018 values were missing and only 25,825 observations were available. Among the 213,018 imputed values (89.19% of the observations), any imputed values below the value of 20 (minimum value for the tuition fee variable across private and government schools) or negative values were dropped (275 observations). Lastly, all missing observations for the dependent, key independent and control variables (except for the tuition fee variable) were dropped from the sample. For dependent variables, I dropped the following missing values: 1,134 for "Math Highest Level," 1,027 for "English Reading" and 46,214 for "Local Reading." I dropped 27,234 observations for "Private" (key independent variable). I dropped 44,993 observations for the missing control variables (excluding "Tuition Fee"). As a result, the final sample size is 117,966. 12 According to UNICEF, the youth literacy rate for males in 79.1 percent. The comparable figure for females is 61.5 percent. (UNICEF, 2013) 13 According to the Pakistan Bureau of Statistics, Pakistan's annual per capita income is $1,513, implying that the average Pakistani makes Rs. 13,292 ($126) per month. (Pakistan Bureau of Statistics, 2015) 14 Two extremely high household size values of 7773 and 307 were coded as missing and dropped, as they are extreme outliers. 16

Table 2: Descriptive Statistics for Dependent, Key Independent and Control Variables

Mean SD Min Max Dependent Variables Math Highest Level 0.50 0.50 0 1 English Reading 0.55 0.50 0 1 Local Reading 0.51 0.50 0 1 Key Independent Variable Private 0.34 0.48 0 1 Child Characteristics Female 0.36 0.48 0 1 Age 9.75 3.19 5 16 Grade 3.97 2.90 0 12 Child Taking Paid Tutoring 0.16 0.37 0 1 Tuition Fee 358.55 227.37 20 8000 Household Characteristics Household Size 7.27 3.54 1 74 House Type Mixed 0.30 0.46 0 1 House Type Solid 0.34 0.47 0 1 House Owned 0.91 0.29 0 1 Electricity Connection Available 0.91 0.29 0 1 Mobile Available 0.84 0.36 0 1 TV Available 0.65 0.48 0 1 Nearest School Distance (km) 1.53 14.02 0 2200 Parent Characteristics Mother's Age 36.47 7.20 16 97 Mother's Education Secondary 0.17 0.38 0 1 Mother's Education High School 0.13 0.34 0 1 Mother's Education Some College 0.03 0.17 0 1 Father's Age 41.25 7.98 18 92 Father's Education Secondary 0.21 0.40 0 1 Father's Education High School 0.26 0.44 0 1 Father's Education Some College 0.12 0.32 0 1 The sample size is 117,966, with 15,681 observations for urban areas and 102,285 observations for rural areas. For descriptive statistics disaggregated by region type, refer to Appendix A.

Table 3 shows the difference in characteristics between students who attend government and private schools. Most differences are statistically significant at the one percent level. The table shows that, on average, private school students pay higher monthly fees (Rs.

452.20 versus Rs. 309.33) for schools and are also more likely to take paid tutoring lessons outside of school. Private school students are more likely to have a solid house and have access to electricity, mobile phone and television at home. On average, parents of children who go to private schools have completed a greater number of years of schooling than parents of children who go to government schools. It is noteworthy that many of the differences in key characteristics between private and government schools are similar to the differences in key characteristics between urban and rural areas (Appendix A).

Table 3: Key Characteristics Disaggregated by School Type

Private Government Difference SE Dependent Variables Math Highest Level 0.51 0.49 0.02*** 0.00 English Reading 0.58 0.53 0.04*** 0.00 Local Reading 0.51 0.51 0.00 ns 0.01 Child Characteristics Female 0.39 0.35 0.04*** 0.00 Age 9.22 10.02 -0.81*** 0.02 Grade 3.52 4.20 -0.68*** 0.02 Child Taking Paid Tutoring 0.33 0.08 0.25*** 0.00 Tuition Fee 452.20 309.33 142.27*** 1.33 Household Characteristics Household Size 6.83 7.50 -0.66*** 0.02 House Type Mixed 0.27 0.31 -0.03*** 0.00 House Type Solid 0.52 0.25 0.26*** 0.00 House Owned 0.88 0.92 -0.04*** 0.00 Electricity Connection Available 0.96 0.88 0.08*** 0.00 Mobile Available 0.92 0.80 0.12*** 0.00 TV Available 0.78 0.59 0.19*** 0.00 Nearest School Distance (km) 1.72 1.44 0.28** 0.09 Parent Characteristics Mother's Age 35.52 36.93 -1.41*** 0.04 Mother's Education Secondary 0.20 0.15 0.05*** 0.00 Mother's Education High School 0.23 0.08 0.15*** 0.00 Mother's Education Some College 0.06 0.01 0.05*** 0.00 Father's Age 40.50 41.65 -1.15*** 0.00 Father's Education Secondary 0.19 0.22 -0.03*** 0.03 Father's Education High School 0.34 0.22 0.12*** 0.00 Father's Education Some College 0.19 0.08 0.11*** 0.00 Total sample size is 117,966, with 77,345 observations for government schools and 40,621 observations for private schools.

RESULTS

My regression results are summarized in Tables 5, 6 and 7, which use math, English and local reading test scores as the dependent variable, respectively. Each regression also controls for child, parent and household characteristics. My analysis uses cross-section fixed effects techniques, and I apply progressively stringent fixed effects controls at the district, village and sibling levels. In each table, Model 1 reports the results of my LPM regression without fixed effects, providing an upper limit for the private school relationship with student achievement.

Model 2 displays the results of regressions that include district-level fixed effects, which control for the permanent characteristics of each district, both observable and unobservable, such as the level of urbanization or the extent of corruption within local education bodies and other fixed social, political and geographic differences. Model 3 includes the results of village-level fixed effects regressions, which control for permanent characteristics of the village, such as school quality (French and Kingdon, 2010). Model 4 tests whether the effect of private schooling differs by gender using interactions. Finally, Model 5 shows the results of a sibling- level fixed effects model. By contrasting children who are enrolled in private schools with their siblings who are not, I control for the effect of family background on test outcomes. Robust standard errors are reported beneath all coefficients.

Overall, the results reported in Tables 5, 6 and 7 for the LPM, district-level and village- level fixed effects specifications confirm my hypothesis that private schooling has a small, significant and positive association with test scores. However, the relationship between private schooling and test scores disappears when including sibling fixed effects in the regression.

Nevertheless, it is advisable to exercise caution when interpreting sibling fixed effects, as the information used to estimate the private school coefficient is derived from less than five

percent of the sample members who live in households with at least two siblings, and in which

at least one sibling attends private school and at least one sibling attends public school.

Table 4: Regression Results for Math Test Scores Dependent Variable Math Highest Level (Score 0-1)15 (1) (2) (3) (4) (5) Village FE w/ LPM District FE Village FE Interaction Sibling FE Key Independent Variable Private 0.0554*** 0.0180*** 0.0149*** 0.0120*** 0.00355 (0.00264) (0.00277) (0.00290) (0.00336) (0.00540) Child Variables Female -0.00726*** -0.0101*** -0.00984*** -0.0125*** -0.00593* (0.00229) (0.00223) (0.00217) (0.00272) (0.00315) Female*Private 0.00742* (0.00440) Age 0.0171*** 0.0200*** 0.0282*** 0.0282*** 0.0445*** (0.000823) (0.000816) (0.000844) (0.000844) (0.00141) Grade 0.0938*** 0.0883*** 0.0806*** 0.0805*** 0.0667*** (0.000946) (0.000959) (0.00101) (0.00101) (0.00174) Child Taking Paid Tutoring 0.0388*** 0.0360*** 0.0382*** 0.0383*** 0.0553*** (0.00320) (0.00325) (0.00362) (0.00363) (0.00881) Tuition Fee / 100 0.000244 0.000391 -0.00108 -0.00106 -0.00396*** (0.000711) (0.000696) (0.000705) (0.000704) (0.00149) Household Variables Household Size 0.000303 -0.000392 -0.0416 -0.0387 (0.000320) (0.000353) (0.000394) (0.000394) House Type Mixed 0.0378*** 0.0164*** 0.00749** 0.00748** (0.00291) (0.00311) (0.00354) (0.00354) House Type Solid 0.0585*** 0.0294*** 0.0157*** 0.0158*** (0.00321) (0.00352) (0.00406) (0.00406) House Owned -0.0188*** 0.00178 0.0113** 0.0114** (0.00382) (0.00395) (0.00443) (0.00443) Electricity Connection Available 0.0321*** -0.00441 -0.00951 -0.00949 (0.00419) (0.00465) (0.00615) (0.00615) Mobile Available 0.0190*** 0.0189*** 0.00676 0.00676

15 The dummy variable "Math Highest Level" takes on a value of 1 if the student scores above the average math score of 3.450 and takes on a value of 0 if the student scores below the average. 21

(0.00353) (0.00392) (0.00460) (0.00460) TV Available -0.0182*** 0.00802*** 0.00366 0.00369 (0.00282) (0.00298) (0.00324) (0.00324) Nearest School Distance (km) 0.000186** 0.0357 0.0637 0.0657 (0.0534) (0.0476) (0.000119) (0.000119) Parent Variables Mother's Age 0.00172*** 0.00174*** 0.000136 0.000137 -0.00193 (0.000301) (0.000302) (0.000317) (0.000317) (0.00291) Mother's Education Secondary 0.0112*** 0.00483 0.00810** 0.00810** 0.00847 (0.00328) (0.00331) (0.00335) (0.00335) (0.0376) Mother's Education High School 0.0333*** 0.0206*** 0.0146*** 0.0145*** 0.0769 (0.00397) (0.00408) (0.00420) (0.00420) (0.0561) Mother's Education Some College 0.0350*** 0.0295*** 0.0271*** 0.0269*** -0.0863 (0.00722) (0.00724) (0.00735) (0.00735) (0.0788) Father's Age -0.00142*** -0.00152*** -0.0332 -0.0332 0.00340 (0.000271) (0.000271) (0.000283) (0.000283) (0.00267) Father's Education Secondary -0.00128 -0.0157*** -0.00795** -0.00793** -0.0170 (0.00308) (0.00309) (0.00321) (0.00321) (0.0343) Father's Education High School 0.0158*** 0.00579* 0.0109*** 0.0109*** -0.0446 (0.00319) (0.00319) (0.00333) (0.00333) (0.0369) Father's Education Some College 0.0287*** 0.0278*** 0.0222*** 0.0221*** -0.00784 (0.00441) (0.00449) (0.00475) (0.00475) (0.0490) Constant -0.132*** -0.0940*** -0.127*** -0.126*** -0.256*** (0.00902) (0.00939) (0.0107) (0.0107) (0.0605)

Observations 117,966 117,966 117,966 117,966 117,966 R-squared 0.437 0.476 0.561 0.561 0.768 F-statistic for joint hypothesis: private = 24.16*** female*private = 0 (0.0000) Robust standard errors are in parentheses. P- values are given in parentheses under F-statistics for joint hypothesis tests. *** p<0.01, ** p<0.05, *p<0.1

Math Test Scores

The simple LPM model in Table 5 shows that going to a private school instead of a government school is associated with a 5.54 percentage point increase in the probability of scoring above the average for math test scores. Applying district-level (or village-level) fixed effects reduces the magnitude of this association considerably. This implies that our initial LPM estimate was positively biased due to omission of controls for the characteristics of villages (for example, whether a village is closer to an urban center, has better quality schools or lower levels of corruption within local administrative bodies) that are linked to math test scores and probability of private school attendance. For the direction of the bias to be positive, village characteristics must either be positively associated with both math test scores and the probability of attending private schools, or the relationship of village characteristics with both variables must be negative. For example, a village with proximity to an urban center is likely to have better access to electricity, which could therefore be linked to better test scores. Such a village could also have a better road infrastructure, which could be positively linked to private school attendance. On the other hand, it is possible that a village with high levels of corruption does not spend enough funds on teacher training or school facilities, which could contribute to poor test scores. Similarly, high corruption could be associated with difficulty in establishing private schools due to licensing hurdles, thereby negatively affecting private school attendance.

Both scenarios would result in a positive bias in the private school coefficient. Lastly, the results show that the association between private schooling and math scores of female students is positive and highly significant. In addition, the association between private school attendance and math test scores is larger for females than for males.

Table 5: Regression Results for English Reading Scores

Dependent Variable English Reading (Score 0-1)16 (1) (2) (3) (4) (5) Village FE w/ LPM District FE Village FE Interaction Sibling FE Key Independent Variable Private 0.0686*** 0.0203*** 0.0111*** 0.00738** -0.0186*** (0.00271) (0.00283) (0.00299) (0.00346) (0.00552) Child Variables Female -0.00455* -0.0107*** -0.0131*** -0.0165*** -0.0127*** (0.00235) (0.00229) (0.00223) (0.00278) (0.00321) Female*Private 0.00953** (0.00456) Age 0.0204*** 0.0233*** 0.0291*** 0.0291*** 0.0457*** (0.000837) (0.000823) (0.000855) (0.000855) (0.00142) Grade 0.0837*** 0.0780*** 0.0726*** 0.0726*** 0.0578*** (0.000963) (0.000966) (0.00102) (0.00102) (0.00175) Child Taking Paid Tutoring 0.0534*** 0.0462*** 0.0394*** 0.0396*** 0.0541*** (0.00324) (0.00332) (0.00370) (0.00370) (0.00891) Tuition Fee / 100 0.00138* -0.000224 -0.00116 -0.00113 -0.00469*** (0.000732) (0.000698) (0.000715) (0.000715) (0.00146) Household Variables Household Size 0.00245*** -0.0414 0.0393 0.0430 (0.000336) (0.000371) (0.000408) (0.000408) House Type Mixed 0.0351*** 0.0188*** 0.0108*** 0.0108*** (0.00300) (0.00318) (0.00365) (0.00365) House Type Solid 0.0608*** 0.0353*** 0.0165*** 0.0165*** (0.00330) (0.00359) (0.00417) (0.00417) House Owned -0.0229*** -0.00155 0.00485 0.00492 (0.00390) (0.00407) (0.00458) (0.00458) Electricity Connection Available 0.0444*** 0.00507 0.00137 0.00139 (0.00433) (0.00475) (0.00632) (0.00632) Mobile Available 0.0225*** 0.0227*** 0.0112** 0.0112** (0.00364) (0.00398) (0.00472) (0.00472) TV Available -0.0125*** 0.00998*** 0.00607* 0.00611* (0.00292) (0.00305) (0.00333) (0.00333)

16 The dummy variable "English Reading" takes on a value of 1 if the student scores above the average English reading score of 3.468 and takes on a value of 0 if the student scores below the average. 24

Nearest School Distance (km) 0.000172** 0.0206 0.000102 0.000103 (0.0567) (0.0455) (0.000123) (0.000123) Parent Variables Mother's Age 0.000495 0.00123*** 0.000117 0.000119 -0.00265 (0.000306) (0.000308) (0.000330) (0.000330) (0.00314) Mother's Education Secondary 0.0219*** 0.00687** 0.00497 0.00497 -0.0840** (0.00336) (0.00337) (0.00343) (0.00343) (0.0380) Mother's Education High School 0.0479*** 0.0237*** 0.0139*** 0.0138*** -0.00527 (0.00405) (0.00414) (0.00430) (0.00430) (0.0560) Mother's Education Some College 0.0513*** 0.0218*** 0.0176** 0.0174** -0.149* (0.00745) (0.00746) (0.00754) (0.00754) (0.0779) Father's Age -0.000533* -0.00133*** -0.000245 -0.000245 0.00421 (0.000276) (0.000276) (0.000293) (0.000293) (0.00286) Father's Education Secondary 0.0149*** -0.00353 0.000958 0.000990 0.000160 (0.00316) (0.00315) (0.00330) (0.00330) (0.0362) Father's Education High School 0.0141*** 0.00816** 0.0142*** 0.0142*** -0.0192 (0.00327) (0.00326) (0.00343) (0.00343) (0.0380) Father's Education Some College 0.0127*** 0.0248*** 0.0242*** 0.0241*** 0.0271 (0.00447) (0.00454) (0.00485) (0.00484) (0.0516) Constant -0.114*** -0.0433*** -0.0576*** -0.0566*** -0.163** (0.00924) (0.00958) (0.0109) (0.0109) (0.0650)

Observations 117,966 117,966 117,966 117,966 117,966 R-squared 0.402 0.448 0.536 0.536 0.758 F-statistic for joint hypothesis: private = 16.98*** female*private = 0 (0.0000) Robust standard errors are in parentheses. P-values are given in parentheses under F-statistics for joint hypothesis tests. *** p<0.01,** p<0.05, * p<0.1

English Reading Test Scores

The magnitude of the relationship between private schooling and English test scores is higher across the different regression specifications as compared to the magnitude of the relationship between private school attendance and math test scores. The simple LPM model in

Table 6 shows that going to a private school instead of a government school is associated with a

6.86 percentage point increase in the probability of scoring above average for English test scores. Applying district-level fixed effects reduces the magnitude of the relationship to 2.03 percentage points. Village-level fixed effects further reduce the magnitude of the relationship to 1.11 percentage points. Similar to the results for Math scores, capturing permanent district and village characteristics lowers the estimated coefficient on private schooling, implying that the simple LPM model failed to account for permanent district and village characteristics that have a similarly signed relationship with English reading test scores and the probability of attending private schools. However, after I apply sibling-level fixed effects, I find that there is a negative association between private school attendance and test scores. The implications of this finding are discussed in greater detail in the next section. Lastly, consistent with the findings for math scores, the results show a positive and significant association between private school attendance and English test scores of female students. In addition, the association between private school attendance and English test scores is larger for females than for males.

Table 6: Regression Results for Local Reading Scores Dependent Variable Local Reading (Score 0-1)17 (1) (2) (3) (4) (5)

Village FE w/ LPM District FE Village FE Interaction Sibling FE Key Independent Variable Private 0.0477*** 0.0142*** 0.00979*** 0.00727** -0.00758 (0.00263) (0.00277) (0.00292) (0.00339) (0.00541) Child Variables Female 0.000719 -0.00547** -0.00762*** -0.00999*** -0.00778** (0.00228) (0.00224) (0.00218) (0.00274) (0.00315) Female*Private 0.00654 (0.00441) Age 0.0220*** 0.0239*** 0.0290*** 0.0290*** 0.0448*** (0.000822) (0.000820) (0.000849) (0.000849) (0.00142) Grade 0.0897*** 0.0865*** 0.0820*** 0.0819*** 0.0682*** (0.000945) (0.000960) (0.00102) (0.00102) (0.00174) Child Taking Paid Tutoring 0.0511*** 0.0449*** 0.0441*** 0.0442*** 0.0538*** (0.00319) (0.00327) (0.00361) (0.00361) (0.00882) Tuition Fee / 100 -0.00169** -0.00139** -0.00114 -0.00113 -0.00406*** (0.000720) (0.000701) (0.000736) (0.000736) (0.00145) Household Variables Household Size 0.000260 -0.000460 0.000106 0.000109 (0.000327) (0.000364) (0.000402) (0.000402) House Type Mixed 0.0412*** 0.0272*** 0.0106*** 0.0106*** (0.00290) (0.00313) (0.00358) (0.00358) House Type Solid 0.0593*** 0.0322*** 0.0126*** 0.0127*** (0.00318) (0.00352) (0.00409) (0.00409) House Owned -0.0222*** 0.00330 0.00743* 0.00749* (0.00383) (0.00398) (0.00447) (0.00447) Electricity Connection Available 0.0248*** 0.00521 0.00216 0.00218 (0.00422) (0.00468) (0.00620) (0.00620) Mobile Available 0.00911*** 0.0118*** 0.0129*** 0.0129*** (0.00352) (0.00390) (0.00467) (0.00467) TV Available -0.0146*** 0.000367 0.000912 0.000939

17 The dummy variable "Local Reading" takes on a value of 1 if the student scores above the average local reading score of 3.476 and takes on a value of 0 if the student scores below the average. 27

(0.00280) (0.00296) (0.00326) (0.00326) Nearest School Distance (km) 0.000214*** 0.0385 0.000101 0.000103 (0.0548) (0.0470) (0.000121) (0.000121) Parent Variables Mother's Age 0.00230*** 0.00160*** 0.000468 0.000469 -0.00432 (0.000300) (0.000305) (0.000326) (0.000326) (0.00301) Mother's Education Secondary 0.0192*** 0.00639* 0.00869*** 0.00869*** -0.0256 (0.00326) (0.00329) (0.00336) (0.00336) (0.0385) Mother's Education High School 0.0406*** 0.0251*** 0.0213*** 0.0212*** 0.0430 (0.00395) (0.00405) (0.00420) (0.00420) (0.0512) Mother's Education Some College 0.0435*** 0.0323*** 0.0355*** 0.0353*** -0.0466 (0.00732) (0.00735) (0.00749) (0.00749) (0.0758) Father's Age -0.00103*** -0.00109*** -0.000279 -0.000279 0.00683** (0.000270) (0.000274) (0.000291) (0.000291) (0.00276) Father's Education Secondary 0.00629** -0.00106 0.000959 0.000981 0.00203 (0.00306) (0.00309) (0.00323) (0.00323) (0.0352) Father's Education High School 0.00872*** 0.0107*** 0.0117*** 0.0117*** -0.0374 (0.00316) (0.00318) (0.00334) (0.00334) (0.0356) Father's Education Some College 0.0134*** 0.0311*** 0.0219*** 0.0219*** -0.00640 (0.00438) (0.00449) (0.00475) (0.00475) (0.0491) Constant -0.170*** -0.129*** -0.145*** -0.144*** -0.299*** (0.00897) (0.00936) (0.0107) (0.0107) (0.0606)

Observations 117,966 117,966 117,966 117,966 117,966 R-squared 0.440 0.473 0.558 0.558 0.767 F-statistic for joint hypothesis: private = 12.14*** female*private = 0 (0.0005) Robust standard errors are in parentheses. P-values are given in parentheses under F-statistics for joint hypothesis tests. *** p<0.01, ** p<0.05, * p<0.1

Local Reading Test Scores

The magnitude of the relationship between private schooling and local reading test scores is lower across the different regression specifications as compared to the magnitude of the relationship between private schooling and math and English test scores. The simple LPM model in Table 7 shows that going to a private school instead of a government school is associated with a 4.77 percentage point increase in the probability of scoring above the average for local reading test scores. Applying district-level fixed effects reduces the magnitude of the relationship to 1.42 percentage points. Controlling for village-level fixed characteristics reduces the magnitude of the relationship even further to 0.98 percentage points. The relatively small relationship between private school attendance and local reading test scores could be explained by the fact that private schools tend to focus on learning in English more than government schools. In fact, parents tend to associate the higher quality of private school education with instruction in English (Society for Advancement of Education, 2015). Therefore, it is possible that local language instruction in private schools is not as high-quality as the instruction in government schools. Lastly, consistent with the findings for math and English test scores, the results show a positive and significant association between private school attendance and local reading test scores of female students. In addition, the association between private school attendance and local reading test scores is larger for females than for males.

Control Variables

My estimates of the relationship between private schooling and gender, and between private schooling and indicators of family's wealth, are unsurprising. Boys tend to score higher in math and English than girls across the various specifications. Taking paid tuition outside of

regular school has a statistically significant and positive association with math, English and local reading test scores. Having a brick house instead of a hut is also significantly positively associated with test scores across the three subjects, underscoring the important role of family income. In addition, it is notable that parental education levels play an important role in student performance. In line with the findings by Andrabi et al. (2009), children with mothers who have secondary-level education perform significantly better than children with mothers who have no education. Similarly, children whose fathers have some college education tend to have significantly higher test scores.

DISCUSSION

Overview of Findings

Private schools in Pakistan have expanded dramatically over the past few decades. In light of this fact, policymakers have questioned the role of private schooling in improving access to quality education in Pakistan. While a substantial body of literature finds that private schooling has a positive relationship with test scores, the majority of studies use data that has limited geographical scope. My research uses data from the nationally representative ASER survey to analyze the relationship between private schools and test scores.

The results of my primary specifications suggest that, consistent with previous research

(Alderman et al. 2001; Andrabi et al. 2010; Aslam 2009; Das et al. 2006; French and Kingdon

2010), private schools have a modest and positive relationship with test scores. The regression results for all three tests (math, reading in English and reading in local language) reveal a significant and positive association between private schooling and test scores. However, this relationship disappears when sibling fixed effects are included in the model. While the results for math and local reading test scores are positive and insignificant, the results for English test

scores reveal that going to private schools is associated with a 1.86 percentage point decrease in probability of scoring above the average, implying that there is a private school disadvantage. This result is surprising considering private schools are perceived as focusing more on instruction in English than their government counterparts. However, as explained previously, my sibling fixed effects specifications rely on much less variation than my other specifications to estimate the relationship between private school attendance and test scores.

The mixed results of the sibling fixed effects model highlight the need for more sophisticated identification strategies using nationally representative data to gain a better understanding of the relationship between private school attendance and test scores.

My results are particularly intriguing when I examine the relationship between private schooling and test scores separately by gender. For females, going to a private school instead of a government school is associated with a larger change in probability of scoring above the average (in comparison to males) across all three test types. The association between private schooling and test scores of female students is consistently significant at the one percent level.

According to Andrabi et al. (2006), low-cost private schools tend to hire female teachers residing in the local areas to keep salary costs low. It is possible that the greater presence of female teachers in private schools facilitates productive in-class interaction for female students that may lead to better test performance. In addition, previous evidence by Aslam (2009) has suggested that girls tend to have poorer access to private schooling than boys. However, no prior studies from Pakistan have presented evidence for the differential relationship between private schooling and test scores for boys and girls. This finding provides an opportunity for further research.

Although my analysis controls for various child-, parent- and household-level characteristics, my results may be subject to omitted variable bias. Factors such as parental motivation are hard to measure and are likely linked to both private school attendance and test score outcomes. More motivated parents are likely to send their children to schools that are perceived as having higher quality, that is, private schools. Similarly, parents with higher levels of motivation are likely to invest more time and resources in their child's education, for example, by helping their child with homework. The absence of this variable from the analysis would upwardly bias our coefficient.

Furthermore, parental attitudes and expectations can significantly differ depending on the child's gender. For example, some research has suggested that parents have more confidence in a male child's math abilities than in the abilities of a female child (Stockdale,

1995). It is possible that parents who send a female child to a private school are likely to have less discriminatory attitudes towards girls' education, which could explain the stronger private school effect for girls than for boys. Because my model does not capture unobservable parental attitudes and expectations towards female education, it is possible that the coefficient on private schooling is upwardly biased: parental support and encouragement is likely to be positively linked to private school attendance for females as well as better test scores.

Policy Implications

The policy implications of my study are mixed. In light of the rise of private schools and poor quality of learning in government schools, the debate regarding the role of the public and private sectors in education remains unresolved. However, it is clear that a large and diverse private sector, comprising both elite and low-fee private schools, is an integral part of Pakistan's

educational landscape. In line with the stance of supporters of privatized education, this study presents some evidence that private schooling can help improve access to quality education.

My findings suggest that the impact of private education on female learning outcomes may be meaningful and significant. Therefore, private schools may play an important role in narrowing the gender disparities in achievement. There is a need for better collection and utilization of gender-disaggregated data to further examine the relationship between private schooling and female achievement.

Irrespective of the marginal superiority of private schools in Pakistan, children in both government and private schools have poor learning outcomes on average. The policy solution to Pakistan's education crisis is to understand why both government and private schools are failing to deliver quality education. There is a need to better utilize existing data to understand the reasons for poor learning taking place in Pakistani schools. One important step towards that goal is to gather more detailed and in-depth survey information to understand how private schools can be used to provide accessible and quality education.

REFERENCES Alderman, H., Orazem, P., & Paterno, E. (2001). School Quality, School Cost, and the Public/Private School Choices of Low-Income Households in Pakistan. Journal of Human Resources, 36, pp. 304-326. Retrieved from http://econ2.econ.iastate.edu/faculty/orazem/lahore.pdf Alwin, D. (1991). Family of Origin and Cohort Differences in Verbal Ability. American Sociological Review, Vol. 56, No. 5, pp. 625-638. Retrieved from http://www.jstor.org/stable/2096084 Amjad, R., & MacLeod, G. (2011). Effects of Private, Public, and Private-Public Partnership Schools in Pakistan. Retrieved from http://www.aserpakistan.org/document/learning_resources/2014/Private_Tuitions/Effectivene ss%20of%20PPP%20by%20Ravish%20&%20Gordon.pdf Andrabi, T., Das, J., & Khwaja, A. (2002). The Rise of Private Schooling in Pakistan: Catering to the Urban Elite or Educating the Rural Poor? Retrieved from http://www.innovations.harvard.edu/sites/default/files/2608043.pdf Andrabi, T., Das, J., & Khwaja, A. (2005). Private Schooling: Limits and Possibilities. Retrieved from http://www.hks.harvard.edu/fs/akhwaja/papers/PrivateSchoold_Final_Nov5.pdf Andrabi, T., Das, J., & Khwaja, A. (2006). Students Today, Teachers Tomorrow? The Rise of Affordable Private Schools. Retrieved from http://www.cgdev.org/doc/event%20docs/MADS/ADK_PriSchools_Feb06.pdf Andrabi, T., Das, J., Khwaja, A., & Vishwanath, T. (2007). Learning and Educational Achievements in Punjab Schools (LEAPS): Insights to Inform the Education Policy Debate. Retrieved from http://leapsproject.org/assets/publications/LEAPS_report.pdf Andrabi, T., Das, J., Khwaja, A. (2009). What Did You Do All Day? Maternal Education and Child Outcomes. Retrieved from http://www.hks.harvard.edu/fs/akhwaja/papers/WhatDidYouDoAllDay.pdf Andrabi, T., Bau, N., Das, J., & Khwaja, A. (2010). Are Bad Public Schools Public “Bads”? Test Scores and Civic Values in Public and Private Schools. Retrieved from http://www.hks.harvard.edu/fs/akhwaja/papers/badpublicschools.pdf Arif, G., & Saqib, N. (2003). Production of Cognitive and Life Skills in Public, Private, and NGO Schools in Pakistan. The Pakistan Development Review, Vol. 42, No. 1, pp. 1-28. Retrieved from http://www.jstor.org.proxy.library.georgetown.edu/stable/pdf/41260519.pdf?acceptTC=true

ASER Pakistan (2010). Retrieved from http://pc.gov.pk/usefull%20links/ASER-2010/ASER- 2010%20FAQs.pdf ASER Pakistan. (2013). Retrieved from http://aserpakistan.org/document/aser/2013/reports/national/ASER_National_Report_2013.p df 34

Aslam, M. (2009). The Relative Effectiveness of Government and Private Schools in Pakistan: Are Girls Worse Off? Retrieved from http://web.a.ebscohost.com/ehost/pdfviewer/pdfviewer?sid=f15691d7-2ecc-47cb-84db- 67258d057f06%40sessionmgr4004&vid=4&hid=4101 Dahl, H., & Lochner, L. (2012). The Impact of Family Income on Child Achievement: Evidence from the Earned Income Tax Credit. American Economic Review, 102(5), pp. 1927–1956. Retrieved from http://econweb.ucsd.edu/~gdahl/papers/children-and-EITC.pdf Das, J., Pandey, P., & Zajonc, T. (2006). Learning Levels and Gaps in Pakistan. The World Bank. Retrieved from https://www.openknowledge.worldbank.org/bitstream/handle/10986/8866/wps4067.txt?sequ ence=2

Davis-Kean, P. (2005). The Influence of Parent Education and Family Income on Child Achievement: The Indirect Role of Parental Expectations and the Home Environment. Journal of Family Psychology, Vol. 19, No. 2, pp. 294-304. Retrieved from http://www.rcgd.isr.umich.edu/garp/articles/davis-kean05.pdf Downey, D. (1995). When Bigger Is Not Better: Family Size, Parental Resources, and Children's Educational Performance. American Sociological Review, 60.5, pp. 746. Retrieved from http://search.proquest.com/openview/ce5065d0a332bef44f0dae95fb09784c/1?pq- origsite=gscholar&cbl=41945 Downey, D. (2001). Number of Siblings and Intellectual Development: The Resource Dilution Explanation. American Psychologist, 56(6-7), pp. 497-504. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11413873 Dubow, E., Boxer, P., & Huesmann, L. (2010). Long-term Effects of Parents’ Education on Children’s Educational and Occupational Success: Mediation by Family Interactions, Child Aggression, and Teenage Aspirations. Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2853053/ Duncan, G. & Magnuson, K. (2005). Can Family Socioeconomic Resources Account for Racial and Ethnic Test Score Gaps? The Future of Children, Vol. 15, No. 1, pp. 35-54. Retrieved from https://muse.jhu.edu/login?auth=0&type=summary&url=/journals/future_of_children/v015/15 .1duncan.html Dundar et al. (2014). Student Learning in South Asia: Challenges, Opportunities, and Policy Priorities. Retrieved from http://www- wds.worldbank.org/external/default/WDSContentServer/WDSP/IB/2014/05/23/000442464_20 140523133319/Rendered/PDF/882670PUB0978100Box385205B00PUBLIC0.pdf Education for All National Review Report: Pakistan. (2015). UNESCO. Retrieved from http://unesdoc.unesco.org/images/0022/002297/229718E.pdf

French, R., & Kingdon, G. (2010). The relative effectiveness of private and government schools in Rural India: Evidence from ASER data. Retrieved from http://www.researchgate.net/publication/46463037 Hausman et al. (2014). The Global Gender Gap Report. The World Economic Forum. Retrieved from http://www3.weforum.org/docs/GGGR14/GGGR_CompleteReport_2014.pdf Hanushek, E. (2003). The Failure of Input-based Schooling Policies. The Economic Journal, 113, pp. 64-98. Retrieved from http://hanushek.stanford.edu/publications/failure-input-based- schooling-policies Hanushek, E., & Wößmann, L. (2007). The Role of Education Quality in Economic Growth. The World Bank. Retrieved from https://openknowledge.worldbank.org/bitstream/handle/10986/7154/wps4122.pdf?sequence =1 Jimenez, E., & Tan, J. (1987). Decentralized and Private Education: The Case of Pakistan. The World Bank. Retrieved from http://www- wds.worldbank.org/external/default/WDSContentServer/WDSP/IB/1987/03/01/000009265_39 80623151848/Rendered/PDF/multi_page.pdf Jimenez, E., Lockheed, M., & Paqueo, V. (1991). The Relative Efficiency of Private and Public Schools in Developing Countries. The World Bank Research Observer, Vol. 6, No. 2, pp. 205-218. Retrieved from http://www.jstor.org/stable/3986318?seq=1#page_scan_tab_contents Kattan, R., & Burnett, N. (2004). User Fees in Primary Education. The World Bank. Retrieved from http://siteresources.worldbank.org/EDUCATION/Resources/278200- 1099079877269/547664-1099079993288/EFAcase_userfees.pdf King, E., & Winthrop, R. (2015). Today's Challenges for Girls' Education. The Brookings Institution. Retrieved from https://books.google.com/books?isbn=0815728611 Macpherson et al. (2014). Microeconomics: Private and Public Choice. Muralidharan, K., & Kremer, M. (2006). Public and Private Schools in India. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.168.3057&rep=rep1&type=pdf Nguyen, Q. & Raju, D. (2014). Private School Participation in Pakistan. The World Bank. Retrieved from http://www- wds.worldbank.org/external/default/WDSContentServer/WDSP/IB/2015/06/17/090224b0828c 82e0/1_0/Rendered/PDF/Private0school0participation0in0Pakistan.pdf Pakistan Bureau of Statistics. (2015). Pakistan Social and Living Standards Measurements. Retrieved from http://www.pbs.gov.pk/content/pakistan-social-and-living-standards- measurement

Society for Advancement of Education. (2015). Education Monitor. Retrieved from http://www.cqe.net.pk/pdf/EM2 Education Monitor.pdf 36

Shavit, Y. & Pierce, J. (1991). Sibship Size and Educational Attainment in Nuclear and Extended Families: Arabs and Jews in Israel. American Sociological Review, Vol. 56, No. 3, pp. 321-330. Retrieved from http://www.jstor.org/stable/2096107 Stockdale, S. (1995). Gender Differences in High School Mathematics Achievement: An Empirical Application of the Propensity Score Adjustment. Tooley, J., & Dixon, P. (2005). Private Education is Good for the Poor. Cato Institute. Retrieved from http://object.cato.org/sites/cato.org/files/pubs/pdf/tooley.pdf

UNICEF. (2013). Retrieved from http://www.unicef.org/infobycountry/pakistan_pakistan_statistics.html

The World Bank. (2008). Retrieved from http://www.worldbank.org/en/news/feature/2014/10/29/teachers-hold-key-student-learning- education-review

APPENDIX

Appendix A: Descriptive Statistics Disaggregated by Region Type Urban Rural Difference SE Dependent Variables Math Highest Level 0.63 0.48 0.15*** 0.00 English Reading 0.70 0.53 0.17*** 0.00 Local Reading 0.65 0.49 0.16*** 0.00 Child Characteristics Age 10.01 9.71 0.30*** 0.03 Female 0.44 0.35 0.08*** 0.00 Grade 4.57 3.88 0.70*** 0.02 Child Taking Paid Tutoring 0.24 0.13 0.11*** 0.00 Tuition Fee 518.79 333.95 184.83*** 1.86 Household Characteristics Household Size 5.57 7.53 -1.96*** 0.03 House Type Mixed 0.19 0.31 -0.13*** 0.00 House Type Solid 0.77 0.28 0.50*** 0.00 House Owned 0.75 0.93 -0.18*** 0.00 Electricity Connection Available 0.99 0.89 0.10*** 0.00 Mobile Available 0.99 0.82 0.17*** 0.00 TV Available 0.95 0.61 0.34*** 0.00 Nearest School Distance (km) 1.41 1.55 -0.14 ns 0.12 Parent Characteristics Mother's Age 36.16 36.51 -0.35*** 0.06 Mother's Education Secondary 0.20 0.17 0.03*** 0.00 Mother's Education High School 0.38 0.10 0.28*** 0.00 Mother's Education Some College 0.10 0.02 0.08*** 0.00 Father's Age 40.70 41.34 -0.64*** 0.07 Father's Education Secondary 0.13 0.22 -0.09*** 0.00 Father's Education High School 0.39 0.24 0.16*** 0.00 Father's Education Some College 0.29 0.09 0.19*** 0.00 Total sample size is 117,966, with 15,681 observations for urban areas and 102,285 observations for rural areas.