External Shocks and India's WPI Inflation

Summary: This article outlines the effects of external shocks and their impact on India's Wholesale Price Index (WPI) inflation. It analyzes the factors and channels contributing to inflation's volatile nature in recent years.

Data Source: RBI, Database on Indian Economy

Indian economy, being a small open economy, suffers a lot from external shocks, mainly during the post-pandemic period when the world economy and geopolitics are changing rapidly. The Russia-Ukraine war, sluggish economic growth of the world economy post-COVID-19, financial shock due to changes in monetary policy in developed nations, the constant war-like situation in the Middle East, repeated pirate attacks in the various trade routes, and other factors have influenced the performance of the Indian Economy in recent years. Both the output and prices of the Indian economy are under constant pressure from these external factors. The wholesale price index (WPI) inflation trend provides information about these external shocks. These shocks affect our price and inflation through various channels, such as: 

1) Supply chain disruptions: Economic shocks such as financial crises, policy shocks, and geopolitical conditions like war, which affect trade routes, disrupt the supply chain of both essential and non-essential commodities. This led to higher prices and inflation during 2022.

2) Energy Prices: Following the war, the USA and the European Union imposed sanctions on oil imports from Russia, creating an international oil price shock. Although India continues to import oil from Russia, a significant amount is still imported from other oil-exporting countries. So, India also felt the heat of war through this increased energy price channel.

3) Increased government spending and monetary stimulus: During the pandemic and post-pandemic period, the government had to spend a lot of stimulus packages to combat other external factors. This accommodative fiscal and monetary policy led to volatile prices in the country during COVID-19 and the war.

4) Labor market disruption: During the pandemic, fuel inflation was low, but core inflation and food inflation remained under pressure as a disruption in the labor market created supply-side pressure on prices in India.

5) Global uncertainty and market sentiments: Pandemics and wars affected emerging markets and large economies like the USA. Increased prices in the US forced the Federal Reserve to make drastic changes in its monetary policy stance. The increase in interest rates in the US disrupted international capital flows. Back crises like the fall of the Silicon Valley Bank created another layer of uncertainty among investors. This led to a further increase in the pressure on production and prices of emerging market economies like India.

References:

[1] Phenoxy Cycloposphazene Market: Economic Impact and Analysis - Credence Research. https://www.credenceresearch.com/news/phenoxy-cycloposphazene-market-economic-impact-and-analysis
[2] Q1 2022 Investment Review and Outlook — Forty W Advisors. https://fortywadvisors.com/blog/q1-2022-investment-review-and-outlook

R Code for Graph:

library(ggplot2)

library(gganimate)

library(gifski)

library(tidyr)

months <- seq(as.Date("2017-01-01"), as.Date("2024-07-01"), by = "month")

Crude_Petroleum<-data$Crude_Petroleum

All_commodities<-data$All.Commodities

Food_Articles<-data$Food.Articles

data1<-data.frame(months, Crude_Petroleum, All_commodities, Food_Articles)

data_long <- pivot_longer(data1, cols = c(Crude_Petroleum, All_commodities, Food_Articles), names_to = "variable", values_to = "value")

p <- ggplot(data_long, aes(x = months, y = value, color = variable)) +

  geom_line()  +geom_vline(xintercept = as.Date("2020-03-01"), linetype = "dashed", color = "red")+

annotate("text", x = as.Date("2020-03-01"), y = max(data_long$value), label = "COVID-19", color = "red", angle = 90, vjust = -0.5) +

  geom_vline(xintercept = as.Date("2022-03-01"), linetype = "dashed", color = "red")+

annotate("text", x = as.Date("2022-03-01"), y = max(data_long$value), label = "WAR", color = "red", angle = 90, vjust = -0.5) +

  labs(title = 'Monthly Time Series', x = 'Month', y = 'WPI Inflation') +

  theme_minimal() +

  transition_reveal(months)

animate(p, nframes = 100, fps = 10, width = 800, height = 600, renderer = gifski_renderer("animated_timeseries.gif"))

By

Aryamaan, M.A. Economics (2023-25), Department of Economics, SBSS, Manav Rachna International Institue of Research and Studies, Faridabad.