Are Sticky Prices Costly? Evidence from the Stock Market

Are Sticky Prices Costly? Evidence from the Stock Market

'are sticky prices costly evidence from the stock market' is the title and central phrase used by Gorodnichenko and Weber to frame a test of whether micro-level nominal price rigidity imposes measurable costs on firms. This article summarizes that paper’s question, data, methods, and conclusions in plain language for readers who want a clear bridge between macro price-rigidity debates and asset-market evidence. You will learn why stock returns are useful for testing menu-cost theories, what the authors find, what robustness checks they run, and where to find replication materials.

As of March 2016, according to the American Economic Review, the paper by Yuriy Gorodnichenko and Michael Weber was published in AER after circulation as an NBER working paper. As of its NBER release in 2013, the study drew attention for pairing confidential micro price data with high-frequency stock returns to test whether sticky prices are costly in practice.

Background and motivation

The question embodied in the phrase are sticky prices costly evidence from the stock market sits at the intersection of two literatures: empirical studies of micro price adjustment and macroeconomic theory about how price stickiness affects output and welfare. In macroeconomics, the New Keynesian menu-cost view posits that nominal rigidities—frictions that make firms slow to change prices—generate real distortions when monetary conditions change. Alternative views argue that observed stickiness can be innocuous (for example, reflecting state-dependent pricing or informational frictions) and need not imply large real costs.

Directly observing the welfare cost of price rigidity is hard. The ingenious idea in the target paper is that financial markets aggregate forward-looking assessments of firm profit prospects. If nominal price rigidity is costly, then an exogenous monetary shock (such as a Federal Reserve announcement) should change the distribution of relative prices in ways that cause firms with stickier prices to be more exposed to value and volatility shocks. Thus, stock returns and especially high-frequency return volatility around monetary-policy events become an indirect but informative filter for detecting the costliness of price stickiness.

Authors and publication

Yuriy Gorodnichenko is a macroeconomist known for work on monetary policy transmission, price dynamics, and business-cycle measurement. Michael Weber is an economist whose research spans macroeconomics and asset pricing. Their collaboration produced the paper titled Are Sticky Prices Costly? Evidence from the Stock Market, which circulated as an NBER working paper and was published in the American Economic Review in 2016. The authors also provided an online appendix and a replication package that document data construction and robustness exercises.

Data sources

The empirical exercise combines micro-level price data with high-frequency financial data and standard firm-level controls. Key data sources are:

• Confidential micro price-change observations underlying the Bureau of Labor Statistics’ Producer Price Index (PPI). These micro PPI records let the authors compute firm- and product-level price-adjustment frequencies and construct measures of price stickiness.

• Transaction-level stock-return data (trade and quote data) and CRSP daily and intraday returns used to compute high-frequency return responses and conditional return volatility. These data provide the event-window returns around monetary-policy announcements.

• Compustat and CRSP firm characteristics (market capitalization, book-to-market, sales, profits, and trading volume) used as controls and to form portfolio sorts.

• Replication materials and an online appendix that describe the sample construction, code, and supplementary tables that support the main findings.

Because the PPI microdata are confidential, the paper documents construction steps in the appendix and provides replication code where feasible.

Empirical strategy — overview

The heart of the identification strategy is an event-study design that uses monetary-policy announcements as plausibly exogenous short-lived shocks to nominal conditions. The paper asks: do firms that historically change prices less frequently (i.e., have stickier prices) show larger changes in stock-return volatility or in returns in narrow windows around monetary announcements than do firms that change prices more frequently?

The strategy proceeds in several steps:

1. Measure firm-level price stickiness from PPI microdata.
2. Sort firms into portfolios by stickiness (for example, quintiles or terciles from most flexible to most sticky).
3. Identify monetary-policy events (FOMC announcements and similar scheduled releases) and define narrow event windows (for example, minutes around the announcement) to capture the immediate market reaction.
4. Compute conditional return responses and return volatility for each firm or portfolio in the event windows.
5. Estimate whether stickier-price firms experience greater conditional volatility or differential return responses after controlling for firm characteristics, industry fixed effects, and past return behavior.

This design relies on the idea that monetary announcements are common shocks to which firms with different price-adjustment propensities should respond differently only if price stickiness imposes real costs.

Measuring price stickiness

To operationalize the are sticky prices costly evidence from the stock market test, the paper constructs price-stickiness measures from the PPI microdata. The basic measure is a firm-level (or product-level aggregated to firm) frequency of price changes: the fraction of months (or reporting intervals) in which a firm changes its listed price. Firms with lower frequencies are classified as stickier.

The authors also construct alternative measures to check robustness: median duration between price changes, the share of prices that are unchanged for extended stretches, and indicators for firms that display extreme stickiness relative to their industry peers. Those alternative measures help ensure that results are not driven by a particular summary statistic.

Event identification and timing

Monetary-policy announcements used in the paper include scheduled Federal Open Market Committee (FOMC) announcements and other high-salience Fed communications. The authors focus on very narrow intraday windows around announcement times to isolate the immediate information flow from other confounding news. Narrow windows (minutes before and after the public statement or press conference) reduce the chance that firm-specific news drives the observed effects and make the shock plausibly exogenous.

In short, these high-frequency windows allow the authors to treat the announcements as common surprises for which cross-sectional differences in exposure should reveal the role of price rigidity.

Econometric specifications

Empirical estimation involves two complementary approaches:

• Portfolio-level differences: Sort firms into stickiness portfolios and compute average conditional return volatility and average event-window returns for each portfolio. Simple differences and t-tests provide baseline evidence.

• Regression analysis: Regress firm-level measures of event-window return volatility (or absolute returns) on indicators of price stickiness, controlling for firm size, book-to-market, past volatility, trading volume, industry fixed effects, and other standard controls. The regressions include clustered standard errors and often robust checks for heteroskedasticity.

The combination of portfolio and regression analyses, plus multiple event-window lengths, strengthens causal interpretation by showing a consistent pattern across methods.

Main findings

The headline result is straightforward: firms with stickier nominal prices experience larger stock-return volatility after monetary-policy announcements than firms that change prices more frequently. In other words, the evidence from equity markets supports the idea that price rigidity imposes real costs that matter for firm valuation and risk exposure.

Key takeaways from the empirical results include:

• The conditional volatility of stock returns around monetary-policy announcements is significantly higher for stickier-price firms than for more flexible firms.

• The increased volatility is concentrated in the immediate windows around announcements, consistent with a reaction to common monetary surprises rather than a slow unfolding of firm-specific news.

• The effect remains after controlling for observable firm characteristics such as market capitalization, book-to-market ratio, past return volatility, and trading volume, as well as industry fixed effects.

• Alternative stickiness measures yield qualitatively similar conclusions, suggesting robustness to measurement choices.

• Placebo tests using non-monetary announcement windows or using randomly assigned event times do not produce analogous cross-sectional volatility differences, supporting the interpretation that the observed pattern is linked to monetary-policy surprises.

Collectively, these findings provide empirical support for the menu-cost interpretation: nominal price stickiness is not merely a descriptive feature of price data but imposes measurable costs that show up in asset prices.

Quantitative interpretation and calibration

Beyond statistical significance, the paper interprets magnitudes by relating observed cross-sectional differences in return volatility to the size of menu costs required in calibrated New Keynesian frameworks. The authors show that reasonable menu-cost parameters can generate the observed cross-sectional variation in exposure, making the menu-cost explanation quantitatively plausible.

This mapping from event-study facts to structural parameters is valuable because it links reduced-form empirical regularities to model ingredients that macroeconomists use when assessing policy implications.

Robustness checks and supplementary analyses

The authors run a rich set of robustness checks. Highlights include:

• Varying event-window widths (minutes to hours) to verify that the effect is strongest in narrow windows around announcements.

• Using alternative stickiness measures (frequency, median duration, extreme-stickiness indicators) to ensure results are not measure-specific.

• Controlling for a wide set of firm characteristics (market cap, trading volume, leverage proxies, book-to-market) and adding industry-by-date controls.

• Placebo and falsification exercises that replace monetary-policy announcements with other scheduled releases to confirm specificity.

• Examining whether results are driven by a subset of industries with atypical pricing behavior.

• Providing an online appendix and replication package that document how the PPI microdata were aggregated, how firms were matched to CRSP/Compustat, and the exact code used for main tables.

Together, these checks strengthen confidence that the reported association reflects a meaningful economic channel rather than a spurious correlation.

Theoretical implications

The empirical evidence in are sticky prices costly evidence from the stock market has several implications for macroeconomic modeling and for how we think about monetary-policy transmission:

• Support for menu-cost views: The cross-sectional exposure of stock-return volatility to monetary surprises is consistent with models where nominal rigidities create real costs when relative prices need to adjust.

• Heterogeneity matters: The findings highlight that firm-level heterogeneity in price adjustment behavior translates into heterogeneity in asset-price responses and risk. Macro models that ignore heterogeneity may miss important distributional amplification channels.

• Asset-pricing link: Financial markets can be informative about real frictions. Equity valuations embed forward-looking expectations of profitability and thus serve as a lens into how nominal rigidities translate into firm-level wealth effects.

• Policy relevance: If price stickiness is costly and heterogeneously distributed, monetary-policy announcements can differentially affect firms and industries, implying nontrivial distributional consequences of policy actions.

These implications suggest that macro and asset-pricing fields benefit from cross-pollination: micro price data can sharpen our inference about model primitives that matter for both real activity and financial valuation.

Reception and influence

The paper has been cited by researchers working on price-setting microdata, menu-cost modeling, and the interaction between macro announcements and asset markets. It sits alongside influential empirical contributions that document micro price durations and sales behavior and complements theoretical work that explores state-dependent versus time-dependent pricing. Related influential works include studies that estimate price-change frequencies from barcode and scanner data, and papers that exploit alternative financial-market reactions to infer real frictions.

By demonstrating a clear asset-pricing footprint of price rigidity, this paper has encouraged further research that uses market data to test macro frictions and has informed debates about the quantitative importance of nominal rigidities for business-cycle fluctuations.

Related literature

Readers interested in the broader context will find these strands helpful:

• Micro price-stickiness measurement: Studies that use store-level or product-level data (scanner and barcode data) to document how often prices change and why.

• Menu-cost models and New Keynesian frameworks: Theoretical work that formalizes how small costs of changing prices can generate substantial aggregate effects.

• Financial markets as tests of frictions: Papers that use bond, equity, or option markets to infer macroeconomic frictions or information flow.

• Empirical studies of monetary-policy event effects: A literature that uses high-frequency asset returns to identify monetary surprises and their consequences for asset prices.

Classic references that provide background to these discussions include foundational price-stickiness measurement papers and well-known menu-cost modeling contributions.

Data and replication

The authors provide an online appendix and a replication package that detail data construction and estimation routines. Key practical points for researchers interested in reproducing or extending the analysis:

• The micro PPI data used for measuring price changes are confidential administrative data. Researchers seeking to reproduce the raw price-stickiness measures will typically need BLS access agreements or to rely on aggregated measures the authors provide.

• The replication package documents the mapping from PPI items to firm identifiers and explains how firms are matched to CRSP and Compustat identifiers used for stock-return measures and controls.

• High-frequency trade-and-quote data used for event-window returns are standard in the market-microstructure literature but require care in cleaning and aligning timestamps with announcement times.

Researchers are encouraged to consult the authors’ appendix for operational details and to use the provided replication materials where available.

Limitations and open questions

No single study is definitive. The paper acknowledges and the literature recognizes several limitations and open avenues:

• Event selection: The study focuses on monetary-policy announcements as the relevant shock. While these are attractive for identification, other types of shocks could be explored to test generality.

• Measurement error: Price-stickiness proxies derived from PPI microdata are informative but imperfect. Confidentiality constraints limit external verification of the raw microdata by outside researchers.

• Alternative channels: While the event-study design mitigates confounding, monetary announcements may influence firms via demand, financing, or information channels that interact with price rigidity. Disentangling these channels fully remains a challenge.

• External validity: The paper studies U.S. publicly traded firms during a sample period that may not capture all regimes of monetary policy or international settings. Extending the approach to other countries, sectors, or times is a natural next step.

Addressing these limitations offers promising directions for future work, including combining richer micro price datasets with diverse event types and exploring structural estimations that jointly match price dynamics and asset-market moments.

See also

• Menu costs
• Sticky prices
• New Keynesian economics
• Asset pricing
• Bils & Klenow (empirical price-behavior literature)
• Nakamura & Steinsson (price-change analysis using barcode data)

References

• Gorodnichenko, Yuriy, and Michael Weber. "Are Sticky Prices Costly? Evidence from the Stock Market." NBER Working Paper (2013); American Economic Review (2016).

• Foundational and related studies on price stickiness and measurement, including work on scanner data and micro price changes.

For precise bibliographic citations and access to the authors’ replication materials, consult the journal and the authors’ working-paper versions noted above.

External resources and where to learn more

Key resources to consult (resource names only; see academic databases or journal repositories for access):

• American Economic Review article page for Are Sticky Prices Costly? Evidence from the Stock Market
• NBER working paper entry for the same paper
• Authors’ working-paper PDFs and replication appendix
• SSRN entry for circulation versions

Further reading in macro and asset-pricing journals will contextualize this paper among related contributions.

Practical takeaway and next steps

If you want to explore the implications of are sticky prices costly evidence from the stock market further:

• Review the authors’ online appendix to understand data construction and replication steps.
• Consider how price-stickiness heterogeneity may affect firms you follow—size, industry, and pricing policy matter.
• For readers interested in crypto and Web3 applications, note that the paper concerns US equities and macro price-setting; however, the methodological lesson—that financial markets can reveal real frictions—applies broadly. For trading or custody services in digital assets and on-chain tools, consider learning about secure custodial and wallet options; Bitget Wallet offers solutions for users looking to manage crypto holdings safely and access markets seamlessly.

Further exploration of the material in this article and the cited replication materials can deepen your understanding of how nominal frictions propagate through financial markets.

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