Replication Files for Upping the Ante: The Equilibrium Effects of Unconditional Grants to Private Schools, 2012-2014

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Reference ID

PAK_2012-2014_FPPSIE_v01_M_v01_A_AER

DOI

https://doi.org/10.48529/tmha-tq22

Producer(s)

Tahir Andrabi, Jishnu Das, Asim I Khwaja, Selcuk Ozyurt, Niharika Singh

Collection(s)

The Strategic Impact Evaluation Fund (SIEF) Impact Evaluation Surveys

Metadata

Documentation in PDF DDI/XML JSON

Created on

Apr 06, 2021

Last modified

Apr 06, 2021

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20903

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129

Reports

Pakistan : What's the Best Way to Invest in Private Schools?

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Author(s)	Andrabi,Tahir; Das,Jishnu; Khwaja,Asim Ijaz; Ozyurt, Selcuk; Singh, Niharika
Date	2019-06-01
Country	Pakistan
Language	English
Abstract	Private schools that cater to low-income students are popular with parents seeking alternatives to government schools. But these private schools, which are often owned by local entrepreneurs, may lack the resources and incentives to expand enrollment or improve quality. They generally operate in markets where access to credit is limited and where there aren't loan products tailored to their needs. This means that any improvements have to be financed through school fees or their own funds. When donors and investors step in to provide support to private schools, they tend to focus on larger operators with a chain of schools, which typically implies selective funding to a limited number of schools rather than broad support to the schooling market. Is this the best way to support private schools that cater to poor families? Could supporting the entire market, rather than select schools—or chains—lead to more competitive pressure to invest in quality improvements that promote students’ learning? And is a market for loans for private schools sustainable?
Download	https://documents.worldbank.org/en/publication/documents-reports/documentdetail/353001560873141888/pakistan-whats-the-best-way-to-invest-in-private-schools

Other Materials

Online Appendix to Upping the Ante: The Equilibrium Effects of Unconditional Grants to Private Schools

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Author(s)	T. Andrabi, J. Das, A.I. Khwaja, S. Ozyurt, and N. Singh
Date	2020
Country	Pakistan
Language	English
Download	https://microdata.worldbank.org//catalog/3878/download/50022

Stata .do files

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Description	Do: Contains do-files for replicating the analyses in the paper. There are three files that produce all the analysis in the paper. The list of do-files is given below: • 0_programs: This do-file contains all the macros and program needed to run the analysis do files. • 1_tables_main.do: This do-file produces Tables 1-8 in the paper. • 2a_tables_appendix.do: This do-file produces Tables B1-B20 in the online appendix. Tables that include panels where post double selection Lasso technique is used are produced using file 2b described below. • 2b_tables_appendix _pdslasso.do: This do-file creates a dataset that can be used to implement the post-double selection Lasso procedure (data_school_LassoCleaned.data) and then uses the dataset to run this procedure to check the robustness of our main results. • lassoClean.ado: used in file 2b. This file is part of the package provided with NBER Duflo Machine Learning lecture.
Download	https://www.openicpsr.org/openicpsr/project/118805/version/V1/view

Output

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Description	This folder contains the results from running the do-files above. Each generated file corresponds to a table number in the main paper (Tables 1-8) or the online appendix (Tables B1-B20). These raw table files are either in .xml or .tex formats; we stitch together panels in TeX for tables that have multiple panels and do some additional formatting in Excel for some tables. After processing, these are all compiled in “AER_Tables.tex” or the “excel_tables_AppendixB.xlsx” to get the final formatted tables available in the paper. The folder also has an excel file with the IRR calculations shown in the paper (“IRR_calculations.xlsx”).
Download	https://www.openicpsr.org/openicpsr/project/118805/version/V1/view

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