The catalogue contains study descriptions in various languages. The system searches with your search terms from study descriptions available in the language you have selected. The catalogue does not have ‘All languages’ option as due to linguistic differences this would give incomplete results. See the User Guide for more detailed information.
Facilitating innovative growth of low cost private schools: experimental evidence from Pakistan 2016-2019
Creator
Khwaja, A, Harvard Kennedy School
Andrabi, T, Pomona College
Das, J, World Bank
Study number / PID
853776 (UKDA)
10.5255/UKDA-SN-853776 (DOI)
Data access
Open
Series
Not available
Abstract
The data contain information on 837 low-cost for-profit private schools (LCPS) from three districts in Punjab, Pakistan: Faisalabad, Gujranwala, and Sialkot. The past few decades have seen an exponential increase in the growth of these LCPS globally, and in countries like Pakistan and India, the private sector now commands a large and quickly increasing share of the market. Over forty percent of primary school enrolment in Pakistan is now in LCPS, and students in private schools in Pakistan far out-perform those in public schools. Yet, firm innovation and expansion is constrained for private schools, likely due to a range of supply-side and market level failures.
The main research questions this study and the uploaded dataset seek to answer are: (1) To what extent are schools constrained by finance, and does the type of financing vehicle (loan vs equity) matter? (2) Is LCPS quality improvement constrained by a lack of access to appropriate quality-enhancing products and services, i.e. educational support services (ESS)? (3) Is there a positive interaction between access to finance and the provision of appropriate innovative investment opportunities?
The dataset includes topics such as school administration, facilities, fees, enrolment, student population, finances, and financial expectations and literacy. Schools are uniquely identified using the variables mauza (administrative district) code and school code. While most of the variables are school-level, there are a few individual-level data pieces that were collected from the school owner. For each school we interviewed only one owner, therefore both schools and school owners are identified using the same mauza code and school code ID.Most interventions to improve education in developing countries require spending significant amounts of money on improving the quality of the inputs to the education system. While this is often a useful approach, in countries with weak governments and low tax collection, little...
Terminology used is generally based on DDI controlled vocabularies: Time Method, Analysis Unit, Sampling Procedure and Mode of Collection, available at CESSDA Vocabulary Service.
Methodology
Data collection period
01/03/2016 - 28/02/2019
Country
Pakistan
Time dimension
Not available
Analysis unit
Individual
Organization
Universe
Not available
Sampling procedure
Not available
Kind of data
Numeric
Data collection mode
The majority of data collection took place on pen and paper, however some sections were collected on tablets using the program Open Data Kit (ODK).We arrived at the sample of schools included in this report through a multi-stage process, beginning with a complete listing of private schools from our previous work in the area. First, we limited this list to private schools in rural areas of Faisalabad, Gujranwala, and Sialkot districts. These districts were chosen for logistical reasons since they are (or border) districts in which we have previously worked (randomly drawn from districts in the Punjab), and we restricted to rural areas primarily because the financial needs of urban schools are significantly larger than our partner can prudently offer. We then arrived at our final sample by further restricting to schools that had expressed interest in receiving financing, which was done to decrease our required sample size since we expect take-up rates to be close to 60% for this screened-in population (as opposed to 20% for the general population).Treatment/Control randomization was done after the non-interested schools had been screened out, so both groups are completely comprised of schools that expressed a need for financing. Thus, our treatment effect will be unbiased with respect to any school/school owner characteristics that interest in financial services may signal. Using this procedure, we have surveyed 999 schools (total enrolment of 176,030 students) over the course of 4 rounds, of which 908 remain in the final sample. Each round lasted roughly two months, with the exception of Round 2, which took significantly less time due to the smaller number of schools being surveyed.Great care was taken in collecting this data to ensure that it is accurate. For example, enumerators took revenue, enrolment, and posted fee figures directly from the registers whenever possible (registers were available in 94% of schools) rather than rely on school owner memory. Furthermore, enumerators were given manuals with detailed instructions on how to record data under a variety of likely register structures (e.g. how to record enrolment data by grade if the register does not group children by grade) to maintain consistency across schools. Throughout surveying, supervisors (1:3 supervisor to enumerator ratio) made random checks to verify that these procedures were being followed, and all registers were photographed at the time of surveying to ensure that data was accurately recorded. After data had been collected a number of backchecks were conducted to ensure internal consistency.
Funding information
Grant number
ES/N010205/1
Access
Publisher
UK Data Service
Publication year
2020
Terms of data access
The Data Collection is available to any user without the requirement for registration for download/access. Commercial use of data is not permitted.