COVID-19’s effect on the supply of schooling services in developing countries

Last registered on September 21, 2020


Trial Information

General Information

COVID-19’s effect on the supply of schooling services in developing countries
Initial registration date
September 18, 2020

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
September 21, 2020, 11:28 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.



Primary Investigator

Stanford University

Other Primary Investigator(s)

PI Affiliation
Princeton University
PI Affiliation
University of Chicago
PI Affiliation
Princeton University
PI Affiliation
University of Chicago

Additional Trial Information

In development
Start date
End date
Secondary IDs
COVID-19 has upended the education sector, with schools closing and shifting to remote instruction. There is increasing concern that in many developing countries where low cost private schools are a large share of the market that COVID-19 closure could lead to a string of permanent school closures and bankruptcies. We study the potential effects of COVID-19 on the private education sector in two countries. We propose working with the governments in the Dominican Republic, and Peru to conduct a survey of secondary school administrators and high school student households. The survey results will allow us to describe the current and future impacts of closures on students’ learning and to document schools’ beliefs of closure. We will also measure the awareness about government relief packages that could help private schools to remain viable. Then, we will use the results of the survey to design a randomized control trial that aims at increasing application and access to government aid. The intervention will give schools access to advisers who provide guidance on how to navigate the government aid application process. Lessons from this experiment will shed light on the role of government aid in shaping the schooling market structure in times of COVID-19.
External Link(s)

Registration Citation

Allende, Claudia et al. 2020. "COVID-19’s effect on the supply of schooling services in developing countries." AEA RCT Registry. September 21.
Experimental Details


This project studies the COVID-19 pandemic’s implications for the education sector and the potential for policy to mitigate effects. Beyond its large share of overall employment, the education sector is particularly relevant during the crisis due to the dynamics of human capital accumulation. Thus, policies that enable continued learning and provide support to education providers in staying in operation may have high returns.

The pandemic has the potential to affect student learning through less effective remote instruction -- especially if students have limited access to technology -- and disruption to learning environments. These effects are likely concentrated in certain populations. Educational institutions face their own issues. Public school districts rely on revenues from local budgets under fiscal stress. Private schools depend on tuition-paying families who are not necessarily receiving the level of instruction they expected and who might be facing large income shocks. In many low-income countries, these private options constitute an important part of the educational market and their inability to survive the crisis would dramatically reduce students’ schooling options and possibly create school deserts.

We will conduct a survey to document the direct impact of the pandemic on households and schools by collecting multi-country data on:
a) Households:
i) Perceptions of school characteristics before and after the covid-19 emergency, ii) Willingness to pay for online instruction,
iii) Beliefs about the duration and potential impacts of the crisis on their income, b) Schools:
i) The impact of the crisis on tuition payments and potential tuition discount bargaining between the schools’ and the households
ii) Extraordinary investments by the schools
iii) Potential cost restructuring responses,
iv) Awareness of government aid programs,
v) Beliefs about the duration and potential impacts of the crisis on both their and their competitors' probabilities of surviving in the industry.

We plan to collect this data in several countries, at a large scale, low cost, and using tools that do not require in-person contact. To do so, we will exploit the capacity that we, as a team, have been building to implement the fieldwork for related education projects in the Dominican Republic, and Peru.

We implement a randomized design that exploits the awareness of access to government relief programs. Governments from these two countries are implementing an aggressive and unprecedented package of measures to fight the COVID-19 pandemic, including generous aid programs targeting small and medium business. These programs aim at mitigating the negative economic impact generated by the measures of social distancing and closure of commercial establishments. Across the two countries in our sample, these packages provide: i) access to subsidized and government backed loans (e.g. FAE-MYPE and Reactiva in Peru), and ii) cash transfers to pay a share of the salary of formal workers (e.g. FASE in the Dominican Republic).

Although many private schools in our setting are eligible for these relief programs, anecdotal evidence from phone conversation with several principals of private schools suggest that most of them are not aware of the existence of the programs or their eligibility, or do not know how to apply.

Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
We will focus on the following outcomes:
1. Government Aid Programs:
a. School applies for the Government Aid programs available (self reported - follow up survey)
b. School gets the Government Aid programs (admin. data - Min. Finance ) c. Size of the Loan (admin. data - Min. Finance)
2. Financial Situation
a. Revenues (IRS)
b. Workers on payroll (Min. Labor)
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
3. Academic Outcomes
a. Test Scores (provided that the standardized tests are administered this year). 4. Market Structure
a. Enrollment 2021 (Min. Educ).
b. Exit: School remains active in the market in the year 2021 or closes (Min. Educ).
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The project will target the universe of secondary private schools in Peru, and the Dominican Republic, which is a total of roughly 11,000 secondary schools. The baseline surveys will be administered online through Qualtrics, and the one-on-one informational phone calls about the available government programs will be done over the phone. Given the quality of the contact information provided by the different governments, we assume that over 70% of the schools will complete the baseline survey. This leads to a total of 7,700 schools for the experiment.
Private schools within our working universe will be randomly assigned into the following three groups.
T0 = Control (N=2,000)
T1 = Email (N=2,000)
T2 = Email + Call (N=3,000)
T3 = Email + Intensive Counseling (N=700)
The sample groups are distributed unevenly because of cost differences between treatment arms. In particular, the intensive counseling treatment is the most expensive one, as it involves a personalized (virtual) session that helps the principal in the application and then follows up to encourage the principal to complete the process.
Using this design, we will better understand if participation in government aid programs affects future schools decisions such as laying off teachers or closing.
Experimental Design Details
Randomization Method
Rndomization done in office by a computer
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
7,700 schools
Sample size: planned number of observations
7,700 schools
Sample size (or number of clusters) by treatment arms
T0 = Control (N=2,000)
T1 = Email (N=2,000)
T2 = Email + Call (N=3,000)
T3 = Email + Intensive Counseling (N=700)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Institutional Review Board Princeton University
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

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Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials