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

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.

2020-10-01

2020-11-30

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)

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).

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.

Rndomization done in office by a computer

Schools

No

7,700 schools

7,700 schools

T0 = Control (N=2,000)
T1 = Email (N=2,000)
T2 = Email + Call (N=3,000)
T3 = Email + Intensive Counseling (N=700)