COVID-19 and Supply Chain Disruption: Evidence from Food Markets in India†

Abstract

This paper looks at the disruption in food supply chains due to COVID-19 induced economic shutdown in India. We use a novel dataset from one of the largest online grocery retailers to look at the impact on product stockouts and prices. We find that product availability falls by 10% for vegetables, fruits, and edible oils, but there is a minimal impact on their prices. On the farm-gate side, it is matched by a 20% fall in quantity arrivals of vegetables and fruits. We then show that supply chain disruption is the main driver behind this fall. We compute the distance to production zones from our retail centers and find that the fall in product availability and quantity arrivals is larger for items that are cultivated or manufactured farther from the final point of sale. Our results show that long-distance food supply chains have been hit the hardest during the current pandemic with welfare consequences for urban consumers and farmers.

Keywords: COVID‐19; E20; E30; L81; Q11; Q54; food; online retail data; prices; supply chain disruptions.

Figure 1

Mean stringency vs. log (COVID‐19 cases) Notes: The figure gives bin‐scatter based on data until April 16, 2020. The x‐axis is based on log (COVID‐19 cases), and the y‐axis corresponds to Mean Stringency Index for 106 countries. The figure shows India as an outlier as its mean stringency is much higher relative to countries with a similar number of confirmed cases. Data Source: OxCGRT.

Figure 2

Pre‐trends and persistence in vegetables and fruits (2020) Notes: The figures plot the weekly coefficients and their 95% confidence intervals. In Panel (a), the dependent variable takes a value one if a product is available on a day in a city and zero otherwise. In Panel (b), the dependent variable is an inverse hyperbolic sine transformation of quantity arrivals in Mandis (in tonnes) for a commodity. The red (blue) dashed line is the week of the first lockdown (end of first lockdown). Sundays are removed from the weekly analyses because product availability shows large fluctuations on Sundays due to Mandi closures. W1 = March 1–7, W2 = March 8–14, W3 = March 15–21, W4 = March 22–28, W5 = March 29–April 4, W6 = April 5–11, W7 = April 12–18, W8 = April 19–25, W9 = April 26–May 2, W10 = May 3–10. The regressions are weighted to give equal representation to each city and 95% confidence intervals are plotted using clustered standard errors (at product level) for online data. 95% confidence intervals are plotted using robust standard errors for Mandi data.

Figure 3

Persistence and seasonality for edible oils Notes: The figures plot the weekly coefficients and their 95% confidence intervals. In both panels, the dependent variable takes a value one if a product is available on a day in a city and zero otherwise. The above graphs plot the weekly effects for the year 2020 (Panel (a)) and the year 2019 (Panel (b)) using the same set of dates (March 1–May 10). The red (blue) dashed line is the week of the first lockdown (end of first lockdown). We draw red and blue line corresponding to the same weeks in 2019 in Panel (b). Sundays are removed from the weekly analyses because product availability shows large fluctuations on Sundays due to Mandi closures. W1 = March 1–7, W2 = March 8–14, W3 = March 15–21, W4 = March 22–28, W5 = March 29–April 4, W6 = April 5–11, W7 = April 12–18, W8 = April 19–25, W9 = April 26–May 2, W10 = May 3–10. The regressions are weighted to give equal representation to each city and 95% confidence intervals are plotted using clustered standard errors (at product level) for online data. In Panel (a) data are available for all three cities, whereas in Panel (b), it comes from Delhi. Clustered standard errors (at product level).

Figure 4

Seasonality in vegetables and fruits (2019) Notes: The figures plot the weekly coefficients and their 95% confidence intervals. In Panel (a), the dependent variable takes a value one if a product is available on a day in Delhi. In Panel (b), the dependent variable is an inverse hyperbolic sine transformation of quantity arrivals in Mandis (in tonnes) for a commodity. The above graphs plot the weekly effects for the year 2019 using the same set of dates as that in our main analyses but for the year 2019. The red line is the week in 2019 corresponding to the first lockdown week in 2020. The blue dashed line is the week in 2019 corresponding to when the first lockdown ends in 2020. Sundays are removed from the weekly analyses because product availability shows large fluctuations on Sundays due to Mandi closures. W1 = March 1–7, W2 = March 8–14, W3 = March 15–21, W4 = March 22–28, W5 = March 29–April 4, W6 = April 5–11, W7 = April 12–18, W8 = April 19–25, W9 = April 26–May 2, W10 = May 3–10. The regressions are weighted to give equal representation to each city, and 95% confidence intervals are plotted using clustered standard errors (at product level) for online data. 95% confidence intervals are plotted using robust standard errors for Mandi data.