Food, Fuel, and Facts: Distributional Effects of Global Price Shocks

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Arpita Chatterjee show that global commodity price shocks lead to a significant decline over time in Indian household consumption.

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Arpita Chatterjee show that global commodity price shocks lead to a significant decline over time in Indian household consumption.

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This video video discusses a study focused on the distributional consequences of rising global food and fuel prices, particularly in the context of emerging markets like India. As prices rose dramatically in early 2022, researchers became interested in how these increases affected different segments of the population, especially poorer households that rely heavily on food and fuel. The study utilizes extensive micro panel data from household consumption and earnings in India to analyze the dynamic causal effects of these price changes.

Key findings indicate that poorer households experience significantly larger decreases in real consumption due to price increases—approximately a 4% loss for a 3% rise in food prices. The impact is more pronounced with the 2022 average food price rise of around 8%, which could lead to consumption losses of about 12% for the poorest groups.

The analysis also reveals that global commodity price shocks differ in their effects on various income groups. For oil prices, both the poorest and the wealthiest households are most affected, while the middle-income groups show less significant impact. Furthermore, the study suggests a strong pass-through effect of global prices to local prices, affecting consumer behavior and suggesting food items are essential across all income levels, not just the lowest.

In conclusion, the research enhances understanding of how global price changes impact consumption and purchasing power, highlighting the need for policymakers to consider these distributional effects in their monetary and economic strategies.

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X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

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We got interested in, studying this,

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distributional consequences of, global food

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and fuel price increases.

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Basically when,

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this prices started rising very rapidly in the early 2022.

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Uh, so we were, were facing this price rises, ourselves,

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and we were observing,

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consumer behavior across different

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segments of the population.

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And at the same time, a lot of media reports,

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policy documents, you know, started expressing very,

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sort of serious concern about how different segments

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of the population are differently affected by these,

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by these, food and fuel price rises.

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In particular, there was the concern that poor households

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for whom food and fuel are essentials.

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And so there are very few substitution possibilities.

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They're going to face more adverse, adverse consequences.

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And because in emerging markets,

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these poor households are larger share of the population.

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So the distribution effects will in turn,

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have larger aggregate demand consequences.

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So for example, IMA for economic outlook was very vocal,

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with these concerns.

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And at the same time, in many emerging markets,

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including India, which is the context

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of our empirical analysis now, such adverse

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distributional consequences were cited as one

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of the main reasons for argent monetary policy actions

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to, to carbon inflation.

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Um, so motivated by this,

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we started looking into the literature

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and we actually didn't find enough academic

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rigorous academic literature that studies, first of all,

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do global food and prices have ous consumption effects?

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Is there any difference between, you know, which segment

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of the population is more vulnerable to what kind

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of price rises?

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Um, what are the channels to which such effects happen,

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if there is any ous effect?

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And finally, in critically, how do we understand, you know,

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certain components of consumption being essential?

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What does it mean? So these are the questions we laid out,

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to study in the context of India,

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where we were actually very lucky

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to have extensive micro panel data on household consumption,

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and earnings,

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and income at a monthly frequency, which allowed us

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to estimate the dynamic causal effects of global food

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and fuel pricing business.

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It's, related to, I know several branches of literature.

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I think the first one I'll speak about is the aggregate

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macroeconomic effects of commodity prices.

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So the largest literature is on oil prices,

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global oil prices,

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and there are seminal works by Hamilton Kian,

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bomber and, others.

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And that studies, these aggregate macro effects of,

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global oil price prices.

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Now, you know, when we talk about impact of prices,

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one thing is that prices are equilibrium outcomes, right?

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So they're shaped by both demand and supply

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and one of the major things.

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So there's disagreement in this literature on, you know,

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what is the relative importance of demand versus

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Supply side factors in, in the macroeconomic effects.

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But what this literature agrees on is that

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the effects are different depending on whether they're

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driven by demand shocks or supply shocks.

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Okay? So this is put very nicely by Killian in, in the,

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in a Title 11, 2009 paper where he says,

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not all oil price shocks are alike, okay?

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So we take this lesson very seriously

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and, you know, you are going

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to actually separate out the global oil demand

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and supply shocks and focus on the supply

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shock that's estimated.

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The recent estimates by, Hamilton and Mobster,

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and that 2019 paper to look at

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how the supply shock driven oil price increases affect,

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the, distribution of consequences.

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There's a relatively smaller literature on a negative effect

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of, of, oil, sorry, of food price increases.

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There are some influential work by pea.

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So for example, there's a recent paper by Pearson Andi on,

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the nature of climate change, where they find

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that the aggregate macro effects

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of global food price rise driven

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by weather shocks is larger on the emerging markets, in,

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in, in, their particularly vulnerable

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to such global food prices.

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But none of this literature actually studies

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the distribution effects.

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So that has been a more recent, focus,

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including some literature, very important literature

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that looks at the carbon pricing

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and in standard emission standards, how that leads

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to energy, price increases

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and, has, distribution consequences.

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That's a relatively would say nascent literature,

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and that's probably where we fit in the most in terms of,

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where we are coming from.

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Um, the other branch of the literature that's sort

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of very connected is the distributional effect

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of domestic monetary politic shops.

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Uh, so that's, you know, some very important work by Chenko

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and it all in 2017

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where defined contractionary monetary policy shock leads

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to rise in inequality.

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Um, then, more recently that literature has started

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to focus on heterogene heterogeneity along the liquid wealth

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distribution, the share of liquid wealth along, the,

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among the population that's motivated

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by the TRO agent share model.

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Um, but there are most of this literature, you know,

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do not have the monthly consumption

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and earnings data that we use.

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And, and, so that sort

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of limits the studies in certain ways.

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And the other point is, you know,

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the monetary policy literature is looking at

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the distribution consequences of monetary policy,

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but often this monetary policy are motivate, are

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be motivated by the global price rises,

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and if different global price rises have different types

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of distributional consequences,

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and if distributional consequences is something

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that monetary policymaker care about.

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And so in that way, we are contributing to this literature

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as well, because, you need to know where

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the inflation is coming from

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and if there are different in distribution cons

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that needs to be taken into.

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So I think broadly that's, that's where we fit in, in,

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in terms of the literature.

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Our, our, research, you know, the data

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that we are using is a consumer, pyramid

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of household survey, control, CPHS data collected

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by the center for, monitoring Indian economy.

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So it's a very unique

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and reach data that's surveying more than 200,000

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households, on detailed consumption expenditure,

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earnings and income at a monthly frequency since 2014.

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So given that the consumption data is very detailed,

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there are, I think one 50 to three consumption categories.

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So we can construct various categories of consumption.

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So we can look at non durable consumption.

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We can look at full including transport and communication,

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and then various food categories.

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Um, and having the monthly data for household,

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that can be combined with global monthly prices data.

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That's what allows us to estimate the dynamic causal effects

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of the, in a panel, local projection framework,

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which is the setup that we use

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and where we separate out this commodity demand

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and supply shocks using essentially the supply shocks

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as instruments were the global crisis.

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Um, the data we use is from 2014 to 2019

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because, you know, of course the period

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of covid is very disruptive.

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Also, in terms of the survey data collection,

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it's available till 2023,

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but we use it up to 2019, for now, to conduct,

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our analysis.

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Um, now a key component of the analysis is to show

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that effects of weather effects of global,

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commodity price shocks differ along the income distribution.

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Uh, so for this, we're going to use the, we use the real,

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initial per capita income distribution

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to categorize the households into five categories.

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Uh, the lowest income households at the bottom decide the

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low income households are the decides two

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and three of per capita real part capita income,

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initial real part capita income.

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Uh, then the low middle income are

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decals four, five, and six.

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Uh, the upper middle income are

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dec decides seven, eight, and nine.

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And then the top dec decide are basically

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the high income households.

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Um, so these households de default very systematically.

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So, for example, poorer households, the poorest groups are

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below poverty line, they're net borrowers.

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Um, typically as you move up along the income distribution,

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they have lower food share and higher savings rate.

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Um, they also have very rich demographic information,

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you know, so that we can use as sort of fixed effects

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as controls in our, empirical analysis.

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Um, of course there are some limitations

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of the data can talk about that also a little bit later.

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Um, but one of the limitations

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that we address is there are some concerns about the

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representativeness of the data for, in particular for rural,

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rural and poor households.

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So we take that into account

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and, you know, we try to now do, we do everything

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as weighted regressions,

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where we take into account the sample weights as well

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as the non systematic differences in non-response rates,

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rates across households.

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So we do the best that can be done.

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Uh, but despite these limitations, it's really rare

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to have this kind of detailed consumption

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and earnings data at a monthly frequency.

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So, for example, you know, people use the literature uses

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very high quality administrative tax returns data,

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which is of course very high quality on income side,

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but you had very limited consumption

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information in these dataset.

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And also tax returns data is typically,

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at annual frequency.

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Um, then, you know, the cons, cons,

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consumer expenditures survey, the css,

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data from the US has very reached consumption and earnings,

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but it's a rotating panel.

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So it's very rare to have both of all of these components.

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Retail consumption, earnings

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and income at monthly frequency for a quite large,

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very large number of households.

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It's, it's rare. And that's sort of what we,

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you know, exploit in in in our exercise.

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The key headline result that we wanted to,

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see is whether there are heterogeneous consumption effects,

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to, offer global food and fuel price, shocks.

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Uh, so first I'll talk about the, I'll talk about,

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this, the food, component.

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Uh, so we find that a rise in global food prices, it leads

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to a fall in real consumption for all households,

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but these effects are monotonically larger

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for poor households.

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Um, so, and they're also quite economically meaningful.

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So for an average food price rise of 3%, it leads

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to more than 4%, real non durable consumption loss

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for the poorest households, more than 3%

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for the middle income households

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and more than 2.5% in for the richer

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for the high income households.

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Uh, if we use the 2022 average food price rise, which is

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around 8%, then the consumption loss

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for the poorer groups is significantly larger at around 12%.

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And then for the middle income groups, it groups,

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it's also quite large at around 10%.

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Now, if we look at the, oil price impact,

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that has a more nuanced effect on inequality.

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So what we find is that both tails

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of the income distribution, the poorest

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and the two richest income groups, they seem

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to be most vulnerable to oil price increases.

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So at 10% rise in oil prices leads

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to which is the average volatility in our data leads

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to nearly 2% fall in real non durable consumption

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for the poorest group.

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And then for the two high income groups for the middle,

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the two middle groups, the, the effects are negative,

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the median effect is negative,

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but it doesn't appear to be statistically significant.

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So that's kind of the headline result.

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Uh, now before going, to talk about, you know, sort of

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how are the transmission mechanism, wanna mention

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a little bit about, you know, that it's really

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what pointing out

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that there are these heterogeneous effects, especially

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for oil, but also for food.

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It depends on how you separate out the

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demanded supply shops.

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So if we use the just exogenous oil price shop, which,

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you know, basically conflict the demand

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and supply shocks in a panel like oil s local projection,

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rather than in an instrumental variable framework,

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then the consumption effects

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of the reach do not show these negative effects.

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Okay, so just like for aggregate macro effects,

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for distributional consequences too,

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not all oil price shocks are aligned

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to understand the mechanisms behind,

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behind the heterogeneous consumption effects, we first

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of all build the dynamic consumption savings model,

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where, you know, consumers have varied

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initial wealth holdings.

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They have labor, income and earnings

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and consumption is a standard, sort

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of a serious somo aggregator

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of non durable consumption and the waste.

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But then in the non durable consumption, we allow

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for non homoe preferences, which we model essentially

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as a non homoe isel css, using, sort

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of motivated by max work

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between different food components and food.

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The idea is that we are interested in un understanding

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whether these consumption components, food

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or food, whether they're essentials in the sense

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that the relative expenditure on these items are affected

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not just by relative price, but also by total expenditure.

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So that will be the definition of essentials in,

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in this, framework.

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So what we find is that, the heterogeneous pattern

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of effects on real earnings

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that mirror that of the consumption.

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So while all households again show a fall in real earnings

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due to rise in food prices,

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these effects are monotonically larger for the poor houses

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for all prices rise in oil prices erode the real purchasing

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power of the poor and the, and the reach,

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and the upper middle income group.

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Uh, but there is no statistically discernible effects on the

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middle, low middle income group, for example.

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So that's sort of the,

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the heterogeneity in the army's response seems

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to be primarily driving the kind

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of heterogeneity consumption response

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that we are seeing in the data.

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We also see very strong price effects.

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So global price shocks they pass

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through to the local prices.

13:58.165 --> 14:01.545
Uh, so global food prices raise the food component of CPI,

14:01.835 --> 14:03.825
which of course affects headline CPI

14:03.825 --> 14:06.065
because headline CPI gives a lot of weight to,

14:06.525 --> 14:09.265
to the food component, and it also makes food

14:09.445 --> 14:10.985
and its different sub-components,

14:11.195 --> 14:15.315
relatively more expensive relative to flail, oil prices,

14:15.615 --> 14:19.555
of course raise local oil component of CCPI,

14:20.055 --> 14:23.195
but it also, affects the core CPI,

14:23.195 --> 14:24.875
especially in urban areas.

14:25.495 --> 14:27.515
And both of these, both the core effect

14:27.575 --> 14:29.195
and the fuel effect leads

14:29.195 --> 14:30.955
to a rise in the overall overall CPI.

14:31.095 --> 14:34.315
So the past superefficient on local prices are larger

14:34.495 --> 14:36.875
for food, but for oil it's more broad based.

14:36.875 --> 14:39.075
It's not just restricted to the fuel components,

14:39.135 --> 14:42.955
is restricted to, you know, the core, of CPIS as well,

14:42.955 --> 14:46.995
especially in, so that's, that's sort of another,

14:47.015 --> 14:49.315
the relative prices, therefore the price effect

14:49.315 --> 14:51.485
that is going to be reflected in the

14:51.485 --> 14:52.765
consum consumer behavior.

14:53.585 --> 14:56.925
And interestingly, what we find there is that the relative,

14:56.945 --> 15:00.005
expenditure on food shows strong patterns

15:00.005 --> 15:01.165
of non homogeneity.

15:01.705 --> 15:03.645
So this is what we mean by that.

15:04.105 --> 15:07.005
Uh, a rise in the global food price makes food

15:07.145 --> 15:08.685
and its relative, you know,

15:08.705 --> 15:10.765
its components relatively more expensive.

15:11.225 --> 15:14.325
Now, if you only have, standard expenditure switching

15:14.505 --> 15:15.765
as relative prices rise,

15:15.945 --> 15:18.715
you reduce the relative expenditure, right?

15:18.895 --> 15:20.355
But what we find is that

15:20.915 --> 15:23.595
relative expenditure on food relative to well, as well

15:23.595 --> 15:26.755
as relative expenditure on especially some sub-components

15:26.775 --> 15:28.875
of food pulses, sugar,

15:29.365 --> 15:32.555
these actually rise following a rise in the global food

15:32.555 --> 15:33.555
prices, right?

15:33.775 --> 15:38.195
So, so expenditure switching should ex, should, imply

15:38.195 --> 15:41.115
that the relative expenditure falls, but we see a rise.

15:41.495 --> 15:43.795
And which would imply that these food components,

15:43.985 --> 15:46.475
overall food as well as especially some sub components,

15:47.065 --> 15:49.955
it's an essential, and it's not just for the poor,

15:49.955 --> 15:51.115
it's definitely for the poor,

15:51.335 --> 15:54.315
but also for the higher income groups, there is this pattern

15:54.455 --> 15:57.515
of, you know, some sub components of food appearing

15:57.515 --> 15:59.115
as essentials in, in consumption.

16:00.525 --> 16:03.265
So I think in conclusion, we find that, you know, we,

16:03.685 --> 16:07.065
the study significantly enhances our understanding of,

16:07.345 --> 16:10.225
distributional consequences of global price changes.

16:10.725 --> 16:14.225
Uh, so we see that the consequences differ by commodity

16:14.765 --> 16:17.585
and whether we separate out the demand of supply shops.

16:18.005 --> 16:21.265
Um, there is a, a risk pattern of heterogeneity

16:21.265 --> 16:22.785
of global food, price, food

16:22.785 --> 16:24.985
and fuel, price changes on consumption,

16:25.175 --> 16:26.265
real purchasing power,

16:26.685 --> 16:29.545
and there is strong pass through on, local prices.

16:30.165 --> 16:31.505
And finally, we find

16:31.505 --> 16:34.465
that food is an essential in the consumption basket,

16:34.595 --> 16:37.825
especially for poorer emerging markets, such as India.

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Food, Fuel, and Facts: Distributional Effects of Global Price Shocks

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