Visualizing COVID-19’s effect on India’s rural economy

The engine of India lies in its rural areas, where 70 percent of its workforce resides, fueling an economy that makes up 50 percent of total national income (as of the 2011–12 National Sample Survey). Consequently, a better understanding of how COVID-19 has affected this population provides insights into the health of the entire Indian economy. Figure 1 provides an overview of some of the key takeaways from the survey across the six focus areas that were investigated, namely: agriculture, labor and income, consumption, migration, access to relief and health.

These takeaways from three points of time in 2020 highlight that COVID-19 had a profound impact on all aspects of India’s rural economy. As of September 2020, the survey found that 16 percent of households had foregone healthcare and 25 percent of respondent households had reduced their consumption expenditures — both raise justifiable concerns. However, it can be difficult to gain a more nuanced understanding of these impacts from high-level figures. It is for this reason that IDinsight has developed an interactive visualization dashboard.

What is the interactive visualization dashboard and how does it work?

The dashboard allows users to interact with the data collected across three rounds of rapid phone-based surveys between March and September. Based on the inputs provided by the user, the dashboard provides a dynamic view of the data across multiple dimensions including time, gender, occupation, and caste.

Through the various dashboard views, users are able to dive deeper into the contemporaneous data collected by the team. IDinsight hopes that the dashboard enables users to test hypotheses on the impact of COVID-19 on India’s rural economy and identify questions for further investigation and research. All the underlying data, documentation, and survey tools are available for download through the World Bank microdata library.

The dashboard contains two views that allow the user to dissect the data in different ways: by time series and bar chart views. Below we share example policy questions in which a user might use each view:

1. Policy sample question: How has COVID-19 affected men and women’s unemployment in rural areas throughout the pandemic? (Time-series view)

The time-series view on the dashboard provides users with the ability to investigate time trends across any outcome of interest. Users can disaggregate overall findings by various slices or sub-groups (e.g. gender) to understand how outcomes fluctuate by sub-groups over time.

For example, the video above shows how the levels of employment remained below pre-lockdown levels, despite signs of recovery in the labor market as of September 2020. It is evident that female respondents appear to report a greater number of days with no work when compared to men. Furthermore, respondents in Andhra Pradesh on average reported a greater number of days with work despite pre-lockdown levels of unemployment being lower than a number of states.

2. Policy sample question: Which groups of people are relief programs effectively reaching? (Bar chart view)

The bar chart view helps users answer questions they may have about the impact of COVID-19 on specific sub-groups. Users can choose a point in time and two slices of the data to get deep, granular information on any outcome they may be interested in.

For example, a user may want to understand the access to relief programs that various subgroups have had at different points in time since the initial lockdown. The video above shows an analysis of the amount of cash received by households with different levels of likelihood they are in poverty, membership in self-help groups, and across states. It appears that in general, households with a greater likelihood of being in poverty on average received a greater amount of cash transfers across states in both May 2020 and July 2020. Additionally, in May 2020 households who were part of self-help groups received a lower level of cash transfers than those households that were not part of self-help groups.

The dashboard can be used to answer other pertinent questions that provide insight for policymakers and other actors as they design programs and services for India’s rural communities. Below are some example questions users can explore using the dashboard. Use the comments to share your findings or highlight gaps for further inquiry.

• Has there been differential borrowing by caste?
• Did weekly income rebound back to pre-COVID levels for any sub-groups?
• Which states had the highest rates of female unemployment?
• Are households with a higher likelihood of being in poverty more or less likely to undertake COVID-preventative behaviors?
• Are members of self-help groups less likely to suffer from food scarcity risks?

This project has been co-funded by South Asia Research Hub — Foreign, Commonwealth and Development Office (FCDO), Government of UK. The views expressed in this post do not necessarily reflect the UK Government’s official policies.