Rising prices, especially for energy, have been squeezing households’ purchasing power, yet with large differences reflecting differences in the rate of inflation, its breadth across consumer items, and the spending structure of the average household. A recent OECD paper (Causa et al. 2022) estimates the inflation-induced decline in households’ purchasing power to range from around 3% in Japan to 18% in the Czech Republic (Figure 1). The effect of rising energy prices is large but differs across countries, being particularly important in Italy, Denmark, and the UK. Rising food prices have been weighing less than rising energy prices on the purchasing power of the average household. Mexico is an exception given the high share of food in the consumption basket and the relatively mild increase in energy prices over the period. Prices of ‘non-food, non-energy’ items have also been depressing the purchasing power of the average household, especially in countries with relatively more broad-based inflation, like the Czech Republic and the US.
Figure 1 Purchasing power changes for the average household (%)
Note: The chart shows that in Italy, for example, the average household experienced a 10.2% decline in purchasing power following changes in consumer prices between August 2021 and August 2022. This is driven by three effects: the effect of changes in energy prices (a 5.3% decline in purchasing power), the effect of changes in food prices (a 2.8% decline in purchasing power), and the effect of changes in non-energy non-food consumer prices (a 2.1% decline in purchasing power).
Source: National HBS and CPI.
This average household picture hides significant differences across socioeconomic groups, reflecting differences in the composition of household spending. For instance, the share of expenditure devoted to energy and food is largest at the bottom of the distribution and declines almost monotonically with household income (Figure 2).
Figure 2 Expenditure shares on energy and food by income group (%)
Note: Distribution based on household income (see Causa et al. 2022 for country-specific definitions of income brackets). Energy refers to energy from housing and private transportation. Food includes food and non-alcoholic beverages, with the exception of Mexico, where it also includes alcoholic beverages consumed at home.
As a result, low-income, rural, and senior households are more exposed to rising inflation than the average household (Figure 3):
- Inflation weighs relatively more on low- than high-income households, but with marked differences across countries irrespective of differences in inflation (Figure 3, Panel A). The gap between low- and high-income households is largest in the UK, while it is close to zero in the Czech Republic and Denmark despite similar or even higher headline inflation over the period covered.
- Energy price inflation is strongly regressive in all countries except Mexico, where it weighs relatively more on high-income households; this is consistent with the fact that in Mexico the share of spending on energy increases across the distribution.
- Food price inflation is also regressive, but less so than energy price inflation in most countries covered. Mexico stands out again, since food price inflation is the single major driver of regressivity. The disequalising effect of food price inflation is also more marked than that of energy price inflation in the Czech Republic and Spain.
Living on limited income is not the only – and often not the first – factor contributing to vulnerability to the current inflationary picture. Living in a small, isolated village is a major vulnerability factor. Inflation tends to disproportionately affect rural households and thus to amplify spatial inequalities. In most countries, the purchasing power gap between rural and metropolitan households tends to be larger than that between low- and high-income households, and this gap is driven by energy (Panel B). Age is another factor of vulnerability to energy and food price inflation, as indicated by the finding of larger purchasing power losses for senior relative to prime-aged households in all countries except Denmark and Spain (Panel C). But age-related gaps are generally lower than place-of-living and income-related gaps.
Figure 3 Differences in purchasing power effects beween various types of households (percentage points)
Panel A. Differences between households’ income groups
Panel B. Differences between households’ places of residence
Panel C. Differences between households’ age groups
Note: The charts show that in the United Kingdom, for example, the decline in purchasing power following changes in consumer prices between August 2021 and August 2022 was 3.1 percentage points higher for low relative to high-income households (a -3.1 percentage point gap). This total gap is driven by three effects: the effect of changes in energy prices (a -2.9 percentage points gap), the effect of changes in food prices (a -1 percentage points gap) and the effect of changes in non-energy non-food consumer prices (a 0.8 percentage points gap). Due to limited data availability, Mexico cannot be covered in Panels B and C, and Germany in Panel B. See the Annex in Causa et al. (2022) for country-specific definitions of high- versus low-income, rural versus metropolitan, and senior versus prime-aged households (age always refers to that of the household reference person).
Source: National HBS and CPI.
Governments across advanced and emerging-market economies have rolled out significant support to shield households and firms from the impacts of high energy prices. Support measures fall under two categories: income support (including transfers and tax credits to consumers) and price support (including reduced taxes and reduced or regulated prices). As documented in OECD (2022) and illustrated in Figure 4, price support has been dominating income support and has been largely untargeted. Income support tends to be targeted to vulnerable households, mostly on the basis of income and in some cases on the basis of age or disability status.
Figure 4 Governments’ responses to cushion households from rising energy prices
Number of measures tallied
Note: Information on 284 measures was collected for 42 OECD and key partner economies. The period covered is October 2021 – December 2022. See OECD (2022) for details.
Source: “OECD tracker of policy responses to energy price shocks”, prepared by Assia Elgouacem (Economics Department), and Hamza Belgroun and Grégoire Garsous (Trade and Agriculture Directorate) with inputs from the OECD’s Economics Department Country Desks.
We argue that government support to cushion high energy prices should become more targeted. While relatively simple to introduce and communicate, measures that act to lower the price of energy weaken incentives to reduce energy use when supply is tight. If prices remain elevated and as they become more volatile in the transition to a low-carbon economy, governments need to shift to more targeted measures, especially income support for vulnerable households. Well-designed income support can limit the burden on government budgets as they preserve price signals for energy savings while providing a financial lifeline to those who need it the most. This policy approach may improve resilience to price swings and energy efficiency in the longer term. Similar policy insights have been drawn from country-specific studies, for example in the case of Italy (Curci et al. 2023).
Well-targeted policy support, though administratively complex, is crucial although sometimes insufficient. As vulnerability to high energy prices is multi-dimensional as opposed to purely income-driven, targeting should also consider other important dimensions, such as the area of residence, housing quality (e.g. energy efficiency), or access to public transport. Income support policies based on household fossil-fuel energy consumption shelter households from rising energy prices but weaken incentives to consume less carbon-intensive energy. Over time, priority should be given to supporting consumers, especially the most vulnerable ones, to adapt their energy consumption and shift to alternative sources of energy.
Reforms in this area need also be made more acceptable than in the past, keeping in mind that clear communication of the incentives and of the intended use of resources raised through carbon taxes helps improve political support for them (Stantcheva et al. 2022).
Last but not least, one key implication from our research is the need to improve the consistency, granularity, and timeliness of the data, as a basis for research and even more for policymaking. Reliable, timely information on consumption patterns would allow the reactions of demand to price shifts and expectations to be quantified. In this respect, the digital transformation may provide an opportunity to build agile targeting instruments based on data collection and management.
Causa, O, E Soldani, N Luu and C Soriolo (2022), "A cost-of-living squeeze? Distributional implications of rising inflation", OECD Economics Department Working Papers, No. 1744.
Curci, N, M Savegnago, G Zevi and R Zizza (2022), “The redistributive effects of inflation: a microsimulation analysis for Italy”, VoxEU.org, 14 January
Stantcheva, S, A Sanchez Chico, B Planterose, T Kruse, A Fabre and A Dechezleprêtre (2022), “Fighting climate change: International attitudes toward climate policies”, VoxEU.org, 14 October.
OECD (2022), As Energy Price Hikes Persist, Better Targeting of Support Becomes Imperative, OECD Publisher.