Sophie Piton, Ivan Yotzov and Ed Manuel

How have profits behaved in this context of sustained level of inflation? In part, the answer depends on how ‘profits’ are defined. Some broad measures suggest increasing profits, but conflate market and non-market sector dynamics and omit important corporate costs. We construct an alternative measure of corporate profits to capture UK firm earnings in excess of all production costs. This measure has been declining since the start of 2022, consistent with evidence from historical energy shocks. This decline has not been uniform across firms, however: firms with higher market power have been better able to increase their margins; others have experienced large declines.

Profits versus excess profits: measurement and trends

Measuring profits is a challenging task: national accounts data are affected by the ‘mixed income’ of self-employed, taxes and subsidies, and conflate two different dynamics: those of the market and non-market (real estate and public) sectors.

We focus on the corporate sector and exclude self-employed and non-market sectors. This is similar to the measure used in Haskel (2023), but in contrast to IMF and ECB work that focuses on the total economy. We additionally abstract from the role of taxes and subsidies; as Haskel (2023) points out, they play a large role in the recent period. We thus focus on corporate gross value added (GVA, at factor costs) and initially split this into two components: employee compensations and ‘profits’, ie everything that is left after paying employees, ie corporate ‘gross operating surplus’ (GOS).

We then go further, following Barkai (2019), and decompose ‘profits’ into two components:

  • Capital compensations, which capture firms’ cost of capital (eg the costs of maintaining the equipment and repaying the debt used to purchase it). Following the Hall and Jorgensen (1967) formula, we measure these costs as the sum of capital depreciation, changes in its replacement cost, and the opportunity cost of holding physical rather than financial capital captured through 10-year government bond yields.
  • ‘Excess’ or, as Barkai (2020) calls it, ‘pure’ profits, ie what a firm earns in excess of all production costs (including not just labour costs, but also the cost of holding and maintaining the capital input). The share of excess profits in corporate GVA (which captures the price a firm sets relative to its average costs) is the closest concept in national accounts to the measure of mark-ups estimated in firm-level data (which captures the price a firm sets relative to its marginal costs).

We start in Chart 1 by looking at trends in the shares of labour and profits in corporate GVA since 2015, and decompose the profit share further into capital/excess profit shares. The profit share is broadly flat over the period, suggesting the IMF and ECB results reflect in part non-corporate sector dynamics, consistent with the findings in Haskel (2023). When decomposing this share into capital and excess profit parts, we can see excess profits increased in 2021 during the rapid post-lockdown demand recovery, consistent with mark-ups increasing during the high demand Covid recovery period. They started to decline, however, in 2022, when the Ukraine war started. This fall in excess profits partly reflects higher capital costs for firms who are now experiencing higher interest payments to service their debt (due to rising interest rates since start-2022).

Chart 1: Profit, capital and excess profit shares, 2015 Q12023 Q1

Percentage of corporate GVA at factor costs

Source: Authors’ calculations using ONS data.

Similarities and differences between current and past energy shocks

In Chart 2.a, we compare the evolution of these shares in the 2022 episode to the oil shocks in the 1970s. In Chart 2.b, we formalise the comparison using a regression framework to estimate the average response of firms’ profits and mark-ups following energy supply shocks over the period 1984–2022 (see Technical appendix for details). We use the oil-supply news shock series from Kanzig (2021) as our measure of an energy shock, identified via the response of oil-price futures in narrow windows around OPEC+ announcements.

Chart 2.b (top panel) describes the responses of labour, capital, and excess profit shares to the identified energy shock. Chart 2.b (bottom panel) estimates the impact on firm-level mark-ups, which is closest in concept to our measure of excess profits in the national accounts. We lack granular data on firms’ mark-ups since the onset of the Russia–Ukraine war, but Haldane et al (2018) estimate mark-ups for all UK-listed firms using data over 1987–2018 from Worldscope. The chart shows the estimated response of average (detrended) mark-ups, weighted by firms’ UK sales.

Chart 2: Profit, capital and labour shares around energy price shock episodes

Chart 2.a: Cumulative changes (percentage points (pp)) of the shares from the first quarter of the shock

Notes: Authors’ calculations using ONS data. The black vertical line denotes quarter five after the shock.

Chart 2.b: Estimated impulse responses of excess profit, capital and labour shares (top panel) and mark-ups (bottom panel) following a 10% increase in oil prices

Notes: Estimated impulse responses to energy shocks, using local projections (see Technical appendix). Results for excess profit, labour and capital shares estimated on quarterly data 1984 Q4–2022 Q4. Results for mark-ups estimated on annual data 1987–2018. Dashed lines denote 90% confidence intervals.

There are two main takeaways from this comparison:

  • The labour/profit share response in the 2022 episode is different from the 1970s but similar to more recent energy shocks.

In the 1970s, the labour share increased in the first couple of years followed by a decline in the following years. The rise and fall in the labour share was, at the time, thought to reflect the failure of wages to adjust to the adverse supply shocks in the short run (Blanchard et al (1997)). Consistent with this story, this pattern was stronger in countries with more rigid labour markets, such as continental Europe and Japan. Blanchard et al (1997) also suggest that while the initial effect of the shock was to decrease the profit share, over time firms reacted by moving away from labour, leading to a steady increase in unemployment as well as a recovery, and even an increase, in profit shares.

By contrast, the labour share is broadly flat in both the 2022 episode and in the local projection results covering the period 1984–2022. This might suggest a different labour market now than in the 1970s, after labour market reforms took place in the 1980s.

  • The excess profit share and mark-ups decrease across all energy shocks, including that in 2022.

All shocks exhibit a significant decline in the excess profit share in the first three years. The excess profit share declines by about 0.7 pp at the peak following a 10% oil price increase in the local projections; it suggests a 14 pp decline following the 200% price increase observed in 1973. By comparison, we observe a peak decline of 20 pp in excess profit after the 1973 oil shock, so the responses are of similar magnitude. Over the five quarters 2022 Q1–2023 Q1, we observe a 3.5 pp decline in the share. While the magnitude might seem smaller this time, the shock is also smaller (40% oil price increase), and the government introduced support schemes to soften the impact. Note that the falling excess profit share reflects at least in part the rise in interest rates – an important component of the cost of capital that rises across all episodes.

This fall also occurs for the average mark-up. The mark-up falls significantly in response to the energy shock with the peak response in year two (by around 0.8 pp), where both the magnitude and time-profile of the response is remarkably similar to our findings for the excess profit share. Ultimately it is mark-ups that matter for inflation, as inflation is proximately driven by changes in marginal costs and changes in mark-ups (over those marginal costs) desired by firms.

Different responses across sectors and firms

The aggregate fall in mark-ups masks significant heterogeneity across sectors: mark-ups rise significantly in the mining and quarrying sector (driven by oil and gas extraction firms), as well as in some other sectors (eg wholesale and retail) – Chart 3.

Chart 3: Estimated response of mark-ups by section-level industry at the one-year horizon

Notes: Dots show estimated response of mark-ups to an energy shock for each industry at the one-year horizon, bands are 90% conf intervals using Driscoll-Kraay standard errors. See Technical appendix for more details.

We extend our local projection framework and interact the mark-up response with firms’ characteristics. We find that, in the year after the energy shock, mark-ups rise by more (fall by less) for firms that are: (i) in more concentrated industries; (ii) less energy-intensive; and (iii) have less sticky prices.

We combine real-time firm-level data from the Decision Maker Panel (DMP) Survey with firms’ balance-sheet data to investigate these firm heterogeneities in the recent period. We lack the required information to estimate mark-ups. Instead, we focus on net operating profit share in sales. Despite some conceptual differences, this measure is closest to the concept for GOS share in gross value added (profit share, including both capital and excess profit), and the two measures commove strongly since the mid-1990s.

We investigate how profits evolve for the average DMP firm, and how they depend on firm characteristics. We find that firms in the DMP experienced a small decline in margins since the start of the Ukraine war (Chart 4). This contrasts with ONS gross profit measure (grey line in Chart 1) that exhibits a flat profit share over 2022–23. This could be related to the broad sample of firms in the DMP; eg there are usually few respondents from the oil sector, where profits tend to increase significantly following an oil shock.

Consistent with the local projections, we see that profits were more negatively affected for firms in high-energy intensive industries, and less negatively affected for firms in more concentrated industries. The DMP also asks about the importance of competitor prices for pricing decisions. Firms that answer that competitors’ pricing is among the most important factor for their price decision usually exhibit a lower decline in margins, but the significance is sensitive to the specification.

Chart 4: Changes in firm profit margins in the DMP conditional on sector/firm characteristics

Notes: The results on profit margins are based on the question: ‘In the first quarter of 2023 (January to March), what was your approximate ‘operating profit margin’ (in percentage terms)? And what was it one year ago, in the first quarter of 2022?’. Energy intensity is estimated using industry data on energy costs from ONS Supply and Use Tables. Industry concentration is measured using a Herfidahl-Hirschman Index at the SIC2 level. The results are weighted by employment and industry shares.

Technical appendix: details on the local projection exercise

In the main post, we use local projection regressions to estimate the response of various outcome variables to energy shocks. First, we estimate the response of a range of aggregate time-series data through the following local projection:

(1) \Delta Y_{t+h} = \alpha^h + \beta^h \epsilon_t + \gamma^h X_t + u_{t}^{h}

where \Delta Y_{t+h} = (Y_{t+h} - Y_{t-1}) is the h-period ahead cumulative change in the outcome variable of interest (eg the profit share), εt is a measure of an energy supply shock (which we take from Kanzig (2021)), and Xt are (lagged) control variables including lags of the dependent variable and other variables capturing the state of the macroeconomy (eg GDP and inflation). Estimates of βh from equation (1) for the labour, capital and excess profit shares, and aggregate (detrended) mark-ups are shown in Chart 2.b in the main post.

We then estimate panel local projections to study the response of firms’ mark-ups at a more granular level. First we estimate the following regressions for subsets of firms in each section-level industry S:

(2) \Delta Y_{i \in S, t+h} = \alpha_{S}^{h} + \beta_{S}^{h} \epsilon_t + \gamma_{S}^{h} X_{i \in S, t} + u_{i \in S, t}^{h}

We then collect and plot the one-year sector-specific mark-up responses \beta_{S}^{1} to highlight the heterogeneity across sectors (Chart 3 in the main post).

Next, we extend equation (2) to investigate the drivers of this heterogeneity:

(3) \Delta Y_{i,t+h} = \alpha_{i}^{h} + \beta^h \epsilon_t + \delta^h \epsilon_t * Z_{i,t} + \gamma^h X_{i,t} + u_{i,t}^{h}

where 𝑍𝑖,𝑡 is a vector capturing a range of potential drivers of heterogeneity in firms’ mark-up response to energy shocks. Table A plots the coefficients for the estimated interaction terms δh at the one-year horizon, where we test for all potential sources of heterogeneity simultaneously. We find significant evidence that mark-ups rise by more (fall by less) for firms that are: (i) in more concentrated industries (as measured by the Herfindahl–Hirschman index from Savagar et al (2021)) ; (ii) less energy-intensive (based on firms’ intermediate consumption of energy goods); and (iii) have less sticky prices (based on firms’ reported frequency of price changes).

Table A: Drivers of heterogeneity in mark-up response

Variable Impact on mark-up response to shock (pp)
(Lagged) mark-up 1.18
Energy intensity -1.03***
Employment 0.09
Concentration 0.62**
Price stickiness -0.73*

Notes: Standard errors calculated using Driscoll-Kraay. Asterisks indicate significance at 99% (***), 95% (**) and 90% (*) level.

Sophie Piton and Ivan Yotzov work in the Bank’s Structural Economics Division.

This post was written while Ed Manuel was working in the Bank’s Structural Economics division.

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