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Learning from past research: Data-driven policy making during the pandemic

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Social distancing in the market. Photo: World Bank / Sambrian Mbaabu Social distancing in the market. Photo: World Bank / Sambrian Mbaabu

This blog entry is part of a series that highlights insights from research for development policies and practices, supported by the Knowledge for Change Program (KCP).

A critical principle that the KCP program tries to promote is that the independent scrutiny of research can strengthen the empirical foundations for policymaking and reduce the emulation of “best” practices that deal with short-term vulnerabilities and challenges. While it is difficult to strike a balance between resilience and agility in policy responses during a crisis, one needs to take an evidence-based approach to constantly assess the impacts of certain policies.  

In this blog, we highlight two projects that leveraged data-driven policymaking processes and simulations, and asked the following questions: 

  1. Face masks are a widely promoted, non-pharmaceutical intervention broadly asserted to curb the spread of COVID-19. Is there systematic evidence in any country that demonstrates that increases in the supply of masks have actually slowed the spread of COVID-19? 
  2. The COVID-19 pandemic threw a devastating blow at the private sector. What would be the simulated effects on firms’ profits, payrolls, exit rates and governments’ tax revenues?  

Informing Evidence-Based Policy Making for Improved Public Health Outcomes through Digitization
As part of a joint effort with government authorities, this research project supported by the KCP, used novel administrative receipt data in Rwanda to produce rigorous impact evaluation evidence for policy decisions. It is the first analysis of its kind in Sub-Sahara Africa, which is comparable to existing work in the United States.

When the pandemic started in Rwanda, the Government licensed and incentivized textile manufacturers to produce certifiably high-quality masks to slow the spread of COVID-19. The study of product level exemptions using transaction data is enabled by the proliferous use of Electronic Billing Machines (EBMs), introduced in Rwanda in 2013. EBMs aim to reduce VAT evasion and accounting costs, strengthen accountability and transparency, elevate government capacity, and accelerate the formalization of the economy. The research team worked with the Rwanda Revenue Authority (RRA) to analyze this novel set of data to study the effectiveness of increasing the supply of high-quality masks in slowing the spread of COVID-19 in Rwanda. 

Overall, the research demonstrated that increased access to formally manufactured masks slowed the spread of COVID-19 in the early stages of the pandemic
More specifically, the researchers used administrative data from the RRA, including EBM receipts, customs, and firm registration, complemented with census data, to study the impact of incentivizing high-quality mask production in Rwanda. Digitally signed and time-stamped EBM receipts collected by the tax authority allowed the researchers to track product-level sales between firms and final consumers. 

Licensing domestic mask manufacturers conservatively reduced mask prices by 8.8 percent and reduced monthly growth in COVID-19 infections (proxied by demand for anti-fever medicine) by 12 percent. The dynamics of the results suggest that increased mask quality explained reduced infections, in a context where there was strict enforcement of mask mandates and informal markets for masks. The analysis suggests that licensing and associated incentives generated social benefits at least 5 times as large as their cost. 

Strengthening data-driven policy making by generating simulated effects of lockdowns on firms and public finances
In the early days of the pandemic, governments struggled with two challenges: 1) what would be the effects of government-imposed restrictions on firms, and 2) how would various support measures help firms cope during the pandemic and alleviate the negative impacts? Using a novel set of administrative corporate tax records from 10 low-and middle-income countries, this KCP project analyzed the direct effects of the lockdowns on firms’ profits, payrolls, and exit rates, along with their implications for tax revenues and government support policies. Three key findings revealed by the project:

  1. Less than half of all firms would remain profitable by the end of 2020, about 5-10% of the aggregate annual payroll would be lost, and the rate of firm exits would on average double. 
  2. While wage subsidies were a widely discussed policy tool to mitigate formal employment losses, wage subsidies would largely be inefficient for countries in Sub-Saharan Africa and would be useful to protect employment only in moderately impacted sectors in middle-income countries. 
  3. On average across countries, even an optimistic scenario (lower-bound predictions) would suggest that only half of all firms would remain profitable, tax revenues remitted by corporations would fall by 1.5% of GDP, and aggregate corporate losses would increase by 2.9% of GDP. 

Each country’s situation is different, so country-specific requests were included to generate customized policy notes for Albania, Costa Rica, Ecuador, Eswatini, Ethiopia, Guatemala, Montenegro, Rwanda, Senegal and Uganda. In Albania for example, the team estimated the effect of changing the size threshold that determines the corporate income tax bracket. In Ecuador, the government requested a training on using administrative data to perform further simulations. To provide more insightful analysis, the team is currently updating the data to compare their predictions with realized data. 

The two projects provided rigorous evidence on the actual or simulated effects of familiar policy tools during a crisis, such as mask requirements and wage subsidies. Findings and recommendations from this research are being utilized by governments and form the foundation of evidence-based policy making. To establish to what extent these simulations were accurate, the project is updating the data to compare their predictions with the realized data. 

The authors would like to acknowledge contributions from the following projects under the guidance of task team leads (TTLs) and researchers: Recording Small Receipts: Digital Technology Adoption at the Margin of Formalization (TTL: Astrid Maria Theresia Zwager/Florence Kondylis); Cross-Country Firm Dataset Built from Administrative Tax Return Data (TTL: Pierre Bachas)

The Knowledge for Change Program (KCP) has launched a blog series to retrospectively highlight a selection of research projects conducted over the past 20 years, many of which remain highly relevant and offer great lessons for development policies and practices today. Managed by the Development Economics Vice Presidency of the World Bank (DEC), the KCP promotes evidence-based policy making through research, data, and analytics. To celebrate the KCP’s fourth phase launched in November 2020, this blog series will investigate the wealth of knowledge researchers have generated in KCP’s previous phases, distill lessons learned, and inspire discussions on future research directions.


Kerina Wang

Senior Program Officer, Development Economics and Chief Economist

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