How do firms set prices? What factors do they consider, and to what extent are cost increases passed through to prices? While these are important questions in general, they become even more salient during periods of high inflation. In this blog post, we highlight preliminary results from ongoing research on firms’ price-setting behavior, a joint project between researchers at the Federal Reserve Banks of Atlanta, Cleveland, and New York. We use a combination of open-ended interviews and a quantitative survey in our analysis. Firms reported that the strength of demand was the most important factor affecting pricing decisions in recent years, while labor costs and maintaining steady profit margins were also highly important. Using three methodological approaches, we consistently estimate a rate of cost-price passthrough in the range of 60 percent for the representative firm over 2022-23—with considerable heterogeneity in this number across firms.
We used a two-stage approach to study firms’ pricing behavior. In the first stage, we conducted open-ended, semi-structured interviews with around thirty business decision-makers (CEOs, CFOs, and business owners) from a variety of industries in 2021 to talk about their price-setting behavior. The findings from these interviews informed a second-stage quantitative survey of about 700 businesses that was fielded in late 2022 and early 2023 across the Second, Fourth and Sixth Federal Reserve districts. We present many preliminary findings from this project here; in this post, we discuss a few highlights from the study.
What Are the Most Important Factors in Pricing Decisions?
The chart below reports the share of firms that view various factors as “important” or “very important” in determining their price-setting decisions. The strength of demand, labor costs, and maintaining steady profit margins are the three most-cited factors, with competitors’ prices and nonlabor costs also cited by a majority of firms.
Our open-ended interviews shed some light on how these factors affect prices. The most common connection between the strength of demand and price-setting is that before deciding whether to pass through a given increase in costs, a firm will assess what effect this increase in prices will have on sales—in some cases by explicitly estimating the elasticity of demand. Many respondents report that they have explicit targets for profit margins or set prices as a fixed markup over costs, consistent with the importance of maintaining steady profit margins and of carefully monitoring their own costs. In general, our interview responses suggest that firms set variable markups in response to demand conditions—consistent with some models of price-setting.
Determinants of Pricing Decisions
How Are Cost Increases Passed Through to Prices?
We obtain estimates of cost-price passthrough using data from our quantitative survey in three different ways. First, we run a regression of the reported percent change in prices on the reported percent change in costs over the last twelve months, controlling for firm sector and size fixed effects. Because the survey was fielded around the end of 2022, this “backward-looking” approach yields a measure of passthrough over most of 2022. Second, we compute a “forward-looking” measure, regressing the expected percent change in prices on the expected percent change in costs over the next twelve months.
The table below summarizes our findings. Columns 1 and 2 show that the estimated average passthrough is around two-thirds in the backward-looking case and only slightly higher (about 69 percent) in the forward-looking case. Adding past price and cost changes to allow for possible lags (column 3) lowers the estimated passthrough to about 63 percent. In addition, we find that controlling for firms’ own inflation expectations (column 4) has little effect on our estimates of passthrough. Expectations about aggregate inflation have a statistically insignificant effect on future prices, suggesting that aggregate inflation is not an important factor in firms’ pricing decisions after we control for expected changes in costs.
Regression-Based Estimates of Cost-Price Passthrough
|Variable||Realized change in prices||Expected change in prices||Expected change in prices||Expected change in prices|
|Expected change in costs||0.687***||0.631***||0.642***|
|Realized change in costs||0.662***||0.018||0.007|
|Realized change in prices||0.103***||0.105***|
|Expected year-ahead inflation||0.059|
Notes: *** denotes statistical significance at the 1 percent level. Each bolded figure in the table reports the percentage change in realized or expected prices associated with a 1 percentage point change in realized or expected costs.
These regression-based estimates identify passthrough based on the cross-sectional correlation between firms’ actual or expected change in prices and change in costs. However, this strategy may not correctly identify the causal effect of a change in costs on prices if omitted variables, such as the strength of sectoral demand, drive the cross-sectional variation in both prices and costs. Thus, our third approach to estimate passthrough was based on the following hypothetical scenario posed to respondents: if your cost growth over the next twelve months were five percentage points higher than what you currently anticipate, then by what percent would you expect to change your prices? We then compute hypothetical passthrough estimates as the difference between the reported hypothetical price changes and the baseline expected price changes, expressed as a share of the five-percentage-point hypothetical cost increase. Using this approach, average passthrough is 50 percent and the median is around 60 percent, similar to our earlier regression-based estimates.
The above estimates are for the average or typical firm, but there is wide dispersion in the distribution of passthrough estimates across firms, as summarized in the table below. We present reported passthroughs from the hypothetical exercise in the first row. For comparison, in the last two rows we also report the distribution of backward- and forward-looking measures of passthrough across firms, here computed as a simple ratio of the change in prices to the change in costs (either over the past twelve months, or expected over the next twelve months, respectively).
In the hypothetical and backward-looking approaches, most firms report passthroughs strictly below one. That is, most firms expect to experience margin compression following a hypothetical increase in costs, or have already experienced margin compression in 2022. Estimated passthroughs tend to be higher in the forward-looking approach, with almost half of all firms expecting to keep margins steady through 2023. Sizable shares of firms report no passthrough, full passthrough (equal to one), as well as passthroughs that are greater than one across all three approaches.
Our open-ended interviews suggest that some firms do not pass through cost increases because prices are set based on long-term contracts or because they face strong competition. In contrast, other firms cite strong demand as the factor that enables them to expand their margins and to raise prices by more than the cost increase.
Distribution of Passthrough Estimates
Note: Each cell reports the share of respondents reporting a given level of passthrough (in the columns) for a given scenario (in the rows).
Using a research design that combines open-ended interviews with a quantitative survey of about 700 businesses, we find, on average, cost-to-price passthroughs in the 60 percent range during a period of elevated inflation—when firms were intensely knowledgeable about, and focused on, prices and costs. These estimates mask considerable heterogeneity, with some firms reporting a passthrough greater than one. Firms report that the key determinants of their pricing decisions include the strength of demand, maintaining steady profit margins, labor and nonlabor costs, and competitors’ prices. The interplay among these various factors and how they contribute to the observed heterogeneity in passthroughs is an important topic for future research.
Wändi Bruine de Bruin is provost professor of public policy, psychology, and behavioral science at the Sol Price School of Public Policy at the University of Southern California (USC), and director of the USC Behavioral Science and Well-Being Policy initiative.
Keshav Dogra is a senior economist and economic research advisor in Macroeconomic and Monetary Studies in the Federal Reserve Bank of New York’s Research and Statistics Group.
Sebastian Heise is a research economist in Labor and Product Market Studies in the Federal Reserve Bank of New York’s Research and Statistics Group.
Edward S. Knotek II is a senior vice president and director of research in the Research Department at the Federal Reserve Bank of Cleveland.
Brent H. Meyer is an assistant vice president and economist in the research department at the Federal Reserve Bank of Atlanta.
Robert W. Rich is the director of the Center for Inflation Research and a senior economic and policy advisor in the Research Department at the Federal Reserve Bank of Cleveland.
Raphael S. Schoenle is an associate professor of economics at Brandeis University.
Giorgio Topa is an economic research advisor in Labor and Product Market Studies in the Federal Reserve Bank of New York’s Research and Statistics Group.
Wilbert van der Klaauw is the economic research advisor for Household and Public Policy Research in the Federal Reserve Bank of New York’s Research and Statistics Group.
How to cite this post:
Wändi Bruine de Bruin, Keshav Dogra, Sebastian Heise, Edward S. Knotek II, Brent H. Meyer, Robert W. Rich, Raphael S. Schoenle, Giorgio Topa, and Wilbert van der Klaauw, “How Do Firms Adjust Prices in a High Inflation Environment?,” Federal Reserve Bank of New York Liberty Street Economics, June 2, 2023, https://libertystreeteconomics.newyorkfed.org/2023/06/how-do-firms-adjust-prices-in-a-high-inflation-environment/.
The views expressed in this post are those of the author(s) and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the author(s).