Inflation has always been a topical issue for both academics and policymakers. It often overshadows other macroeconomic issues in the news and probably for good reason, as it is one of the key measures that central banks use to guide monetary policy. Sadly, the discussion about inflation has very little to offer for the average individual and that is because the analysis is often reduced to a single measure – the Consumer Price Index (CPI). This might have come about unintentionally: when your mandate is to target CPI, you end up talking about CPI. The issue is when the same measure is extended to broader purposes.
A personal example where limitations of CPI come to mind is the short-lived Cauliflower Revolution in Lithuania. It has an overly dramatic ring to it, but around May last year a movement began in social media, initiated by individuals who were outraged by large prices of cauliflower and other vegetables. This resulted in grocery store boycotts and convoluted statements by politicians trying to explain the situation. How is this tied to inflation? Well, if one looked at the baseline CPI figures at that time, they would have found annualized CPI being close to zero. That naturally spurred further anger in social media and led to conspiracy-like accusations that Statistics Lithuania is secretly collaborating with large retailers in an effort to misinform the public.
Personally, I like conspiracy theories as much as the next guy, but putting my tinfoil hat down for a second, I think there is some nuance that should have been acknowledged at that time. Namely, that each person experiences inflation individually. We can actually see that in the data as well.
To illustrate this example, I will use two datasets: a) monthly inflation data, disaggregated by products and services, and b) 2012 household budget survey which gathered information on Lithuanian household spending habits. The cool side to this (and yes, this is the coolest it can get for me) is that both inflation and survey questions are categorized the same way, making it possible to accurately match both data.
First, by using survey responses, we can look whether there are meaningful differences in spending habits across respondents. Below I plot the share of spending per category by income quantile. While middle and bottom quantiles look similar, we can see an increase in the share of spending for “culture” and restaurants in upper quantiles to, I imagine, sustain their posh habits.
Next, we can combine the information of each respondent’s share of spending with inflation data. By considering their reported spending shares as their “real basket” of goods and maintaining the “real” composition of the basket fixed over time, we can calculate how much the price of each respondent’s basket changed between 2012 January and 2016 November. This can give us a distributional picture of how the price of these baskets changed over time.
What we can see here is that for half of the survey respondents, the cost of their basket of goods did not change much. Setting the value of each basket at 100 at the start of the calculation, the price of the basket of goods for the middle 50% after 5 years was somewhere between 99 and 104. Comparing that to the baseline inflation there is not much difference. However, the interesting bit is the remaining 50%: in its extremes a particular basket of goods had the cost increasing to 137.79, while on the lower end we can find it as low as 87.43.
This is not to say that anyone experienced their purchasing power reduced by 37%, people respond to changes in prices and adjust their baskets accordingly. However, what I wanted to illustrate with this example is that inflation can look very different from the point of view of an individual and going beyond an aggregated measure can make the discussion about inflation more applicable and relatable.