In India, climate change is altering weather patterns, which is impacting about 55% of the country’s inflation basket directly. We think there are implications for incomes and the twin deficits too. And all of this is happening right under our nose.
Temperatures are on the rise, as is the occurrence of extreme weather events. Recall the heatwave in March, which played havoc with the wheat crop, forcing the government to ban wheat exports at a time when world demand was on the rise.
We find that rains have become more volatile, deviating much more from normal than before, as episodes of unseasonal rains and changing monsoon patterns take hold. We had argued a few years ago that reservoir levels matter much more than rains for India’s food inflation. We find that reservoir patterns are also changing alongside rain patterns. An econometric study reveals that, compared to the last ten years, we now get much lower reservoir water levels in July, and far higher levels in August. This is important for food production and inflation. In our previous research we had also found that July reservoir levels are an important determinant of food inflation, because a lot of the sowing happens in that month. But with changing rain and reservoir patterns, sowing practices are likely to change.
In fact, we find that sowing patterns have become far more volatile over time. All of this arguably creates inflationary pressures for food crops, even if temporarily.
We go on to test whether long-held seasonality patterns in food prices are changing. We start with overall food prices, which make up 46% of India’s consumer price basket. We do an econometric study on data from the last decade to identify the traditional seasonality patterns. We find that between April and October every year, food prices used to rise each month.
Repeating the study for only the last three years suggests that the rise in food prices in the April to October period is not as uniform as before. Rather it is bunched up into only a few months, making food price changes more volatile than before. This experience over the summer months is most pronounced for cereal prices. Similarly, in the past, vegetable prices used to fall in the December to February period. This was popularly known as the winter disinflation, with an implicit message to the central bank to not get carried away by the rise in vegetable prices over the summer months; rather to look through it and wait for the winter disinflation in order to get a clearer sense of where food inflation really is.
Now vegetable prices, too, are displaying changing seasonality patterns. Employing the same econometric technique suggests that the vegetable disinflation that was spread out over the December to February months now starts later, and is bunched up into the January to February period. No surprise that vegetable prices remain the most volatile component of the food basket.
With temperatures rising and extreme weather events becoming more frequent, the demand for energy is also becoming more volatile. We try to get a handle on the changing demand for energy due to climate change. We model oil demand with the usual economic variables like GDP growth, the ratio of manufacturing to services, and domestic oil prices. Our regression is economically and statistically significant. But what is most interesting for us is the non-economic drivers of energy demand, which we haven’t captured in our model, but can still get a handle on via the residual term.
The residual term in our regression ends up capturing the other drivers of energy demand, for instance those related to climate change. We extract the residual series and find that it has become far more volatile than before. In other words, once the impact of the usual economic drivers of energy demand are removed, the remaining drivers such as climate change have made energy demand more volatile over time.
It is worth clarifying here that climate change can impact energy demand in several ways. In the first instance, episodes like a heatwave in March, or a colder-than-normal winter, raise demand for energy. Secondly, as the world transitions to renewables, there is likely to be a transition period during which fossil fuel-derived power is dis-incentivised before renewables reach their full potential. This period could be marked by volatile energy prices.
Weather-related surprises are on the rise, making India’s inflation patterns even harder to predict than before. It is therefore no surprise that inflation forecasting errors are on the rise.
With inflation volatility rising, it will become more challenging over time to anchor inflation expectations at desired levels. This, in some situations, may require larger rate hikes in order to remain close to the inflation target, which, in turn, would slow GDP growth. RBI may have to raise rates earlier rather than later in the cycle, as a way of keeping inflation contained without too much cumulative tightening. India may eventually need a coordinated institutional framework tying together the different parts of policymaking in order to navigate the increasing volatility triggered by climate change and energy transition.
(The writer is chief India economist, HSBC. With inputs from Aayushi Chaudhary, India and Sri Lanka economist, HSBC; and Priya Mehrishi, senior associate, Global Research, HSBC)