Markups Across Space and Time

Anderson, Rebelo, and Wong study the behavior of markups in the retail sector across regions and over time, using a combination of firm-level Compustat data and product-level scanner data from two large retailers — one operating over 100 stores across U.S. states (quarterly data from 2006 Q1 to 2009 Q3, covering roughly 3.6 million SKU-store pairs across 79 product categories) and one operating hundreds of stores across Canadian provinces (quarterly data from 2016 Q1 to 2018 Q4, covering 15.6 million item-store pairs across 41 product groups). Markups are measured using gross margins — sales minus cost of goods sold as a fraction of sales — computed at the product level using the replacement cost for every item. This measurement approach is appropriate for retail because cost of goods sold accounts for over 80 percent of total retail firm costs, making it a reliable proxy for marginal cost. The replacement cost data, available at the store level, is the cost used by managers in actual pricing decisions, distinguishing these datasets from typical scanner data that contain only average costs.

The paper documents five main facts. First, markups are remarkably stable over time and display a mild procyclical pattern. At the aggregate level, gross margins are roughly acyclical or mildly procyclical while sales and cost of goods sold are highly procyclical. The elasticity of gross margins with respect to real GDP is statistically insignificant at both the aggregate and firm level. The conditional response of gross margins to high-frequency monetary policy shocks and oil price shocks is also statistically insignificant, while net operating profit margins fall significantly in response to both shocks. Operating profit margins are 3.4 times more volatile than gross margins at a quarterly frequency, and sales and costs are roughly 2.6 times more volatile.

Second, there is large regional dispersion in gross margins. A variance decomposition shows that the regional variance of gross margins (0.103) is substantially larger than the time-series variance (0.013), with a near-zero covariance between the two components. Third, regions with higher incomes and more expensive houses have higher markups — gross margins are positively correlated with log household income and log median house value in both the U.S. and Canadian data.

Fourth, these higher regional markups do not result from less intense competition or regional differences in marginal costs. Gross margins are uncorrelated with the Herfindahl index (a measure of competition) and with a rural dummy (a proxy for higher transportation costs). The cyclicality of markups is acyclical or mildly procyclical regardless of whether the underlying product costs are themselves acyclical, procyclical, or countercyclical.

Fifth, and most distinctively, regional variation in markups arises from differences in assortment composition across regions rather than from deviations from uniform pricing. A decomposition of regional gross margin variance confirms that the dominant component is the term capturing differences in product assortment across markets; the term capturing differences in gross margins for the same item — which would be nonzero under geographic price discrimination — accounts for very little of the regional variation. When the same item is available in different regions, the retailer charges a uniform price, consistent with Della Vigna and Gentzkow (2019).

To rationalize these five facts, the authors propose a model with non-homothetic, quadratic preferences (following Melitz and Ottaviano 2008). In the model, higher-productivity regions choose higher-quality goods, which have less elastic demand and therefore higher markups. The markup is procyclical with respect to productivity shocks (A) but acyclical with respect to labor supply shocks (N), so a mixture of both types of shocks produces mildly procyclical markups. The model generates uniform pricing across regions for the homogeneous good, with regional markup differences arising through quality and assortment selection rather than price discrimination.

Q: How do the authors measure markups, and why is this approach appropriate for retail? A: Markups are measured as gross margins — (sales minus cost of goods sold) divided by sales — computed at the product level using the replacement cost for every item. This is appropriate for retail because cost of goods sold is the predominant variable cost, accounting for over 80 percent of total retail firm costs. The replacement cost is the marginal cost concept used by managers in pricing decisions and is available at the store level rather than as a national average.

Q: What is the cyclical behavior of gross margins at the aggregate retail level? A: Gross margins are roughly acyclical or mildly procyclical. Sales and cost of goods sold are highly procyclical, suggesting that the business cycle primarily affects quantities sold rather than markups. Operating profit margins are 3.4 times more volatile than gross margins at a quarterly frequency, while sales and costs are roughly 2.6 times more volatile.

Q: What is the conditional response of gross margins to monetary policy and oil price shocks? A: The response of gross margins to both high-frequency monetary policy shocks (identified from Federal Funds futures data) and oil price shocks (identified via the Ramey-Vine 2010 VAR approach) is statistically insignificant. In contrast, net operating profit margins fall in a statistically significant manner in response to both types of shocks, indicating that fixed cost absorption rather than markup adjustment drives profit volatility.

Q: How large is the regional dispersion in gross margins relative to their time-series variation? A: The variance decomposition shows that the regional variance of gross margins is 0.103, compared to a time-series variance of only 0.013, with a covariance term close to zero. The vast majority of gross margin variation is therefore cross-sectional rather than time-series.

Q: What variables explain the regional variation in gross margins? A: In the U.S. data, gross margins are positively correlated with log household income and log median house value. Gross margins are uncorrelated with the Herfindahl index (a competition measure) and with the rural county dummy (a transportation cost proxy). Canadian data confirms the positive correlation between gross margins and both log household income and log median house value.

Q: What is the mechanism through which higher-income regions have higher markups? A: Regional markup differences are driven by assortment composition differences, not price discrimination. When the same item is sold in multiple regions, it sells at a uniform price. Higher-income regions carry different (higher-quality, higher-margin) products. The correlation between unique items sold and regional household income is 0.42 for the Canadian retailer and 0.17 for the U.S. retailer.

Q: How is the variance of regional gross margins decomposed into assortment versus pricing components? A: The variance decomposition separates total regional gross margin variance into: (1) a term for differences in gross margins for the same item across regions (would be nonzero with geographic price discrimination), (2) a term for differences in assortment composition holding gross margins fixed, and (3) an interaction term plus covariance terms. The dominant term is the assortment composition component; the same-item price difference term accounts for very little of the regional variation.

Q: Does the acyclicality of gross margins hold for products with procyclical costs? A: Yes. The authors divide products into those with acyclical, procyclical, and countercyclical costs and show (Table 7) that gross margins are acyclical or mildly procyclical for all three groups in both the U.S. and Canadian data. This implies that retailer pricing behavior contributes to price inertia even for products whose wholesale costs move with the cycle.

Q: What fraction of gross margin changes are active versus passive? A: In the U.S. data, 91 percent of margin changes are active (resulting from price changes, regardless of whether replacement cost has changed); 9 percent are passive (replacement cost changes with no price change). In the Canadian data, 93 percent of changes are active. Both the probability of active margin changes and the size of margin changes are acyclical with respect to unemployment and local house prices.

Q: How does the Hall approach compare to gross-margin-based markup estimates? A: When the Hall approach is implemented using output elasticities (deflating sales by a product-level price deflator to obtain quantity), the resulting markup estimates are very close to those from gross margins — the ratio is 1.014 for the U.S. firm and 0.991 for the Canadian firm. However, when revenue elasticities are used instead of output elasticities (the common practice in the literature due to data limitations), the implied markup is 14 percent lower for the U.S. firm and 13 percent lower for the Canadian firm, confirming the bias documented by Bond et al. (2020).

Q: What are the key features of the theoretical model and what facts does it explain? A: The model uses non-homothetic quadratic preferences (Melitz-Ottaviano form) in which demand elasticity falls as consumption quality rises. Higher-productivity regions optimally consume higher-quality varieties, which face less elastic demand and hence carry higher markups. The markup is procyclical in productivity (A) with an elasticity less than one (incomplete cost passthrough) and acyclical in labor supply (N), so a mixture of shocks generates mild procyclicality. Uniform pricing across regions for the homogeneous good holds by construction, and regional markup differences arise through quality-assortment selection.

Q: Which existing macroeconomic models are consistent with the time-series evidence, and which are not? A: The evidence is inconsistent with models featuring countercyclical markups (Rotemberg-Woodford 1992 imperfect competition, Ravn-Schmitt-Grohe-Uribe deep habits, Jaimovich-Floetotto entry-exit, and standard New Keynesian models with sticky prices and procyclical marginal costs). The time-series evidence is consistent with models featuring sticky retail prices and acyclical marginal costs (Nakamura-Steinsson 2010, Coibion-Gorodnichenko-Hong 2015) and models with price and wage rigidities at the manufacturing level (Erceg-Henderson-Levin 2000, Christiano-Eichenbaum-Evans 2005). Mildly procyclical search models (Alessandria 2009) are also consistent when procyclicality is mild.

Q: Which existing trade and regional models are consistent or inconsistent with the regional evidence? A: The spatial price discrimination models of Greenhut-Greenhut (1975) and Thisse-Vives (1988), which predict higher markups in less competitive regions, are inconsistent with the data. The Bertoletti-Etro (2017) non-homothetic model predicts that regional markup variation is driven by deviations from uniform pricing, which is also inconsistent. The Fajgelbaum-Grossman-Helpman (2011) model predicts countercyclical markups when costs are procyclical, contradicting the time-series results. Most existing macroeconomic models rely on homothetic preferences, predicting markups independent of regional income, inconsistent with the regional facts.

Q: What are the scope conditions on the measurement approach? A: Gross margins are valid proxies for markups only in the retail sector, where cost of goods sold is the dominant variable cost (over 80 percent of total costs). In manufacturing, where labor and other costs represent a larger fraction of total variable costs, gross margins would not be a reliable markup measure. The product-level scanner data cover the 2006-2009 period for the U.S. and 2016-2018 for Canada; the U.S. sample includes a recession while the Canadian sample covers a moderate expansion.

Gross margin as markup proxy: The ratio of (sales minus cost of goods sold) to sales, computed at the product level using the replacement cost for each item at each store and time period. Used as a proxy for the price-cost markup because cost of goods sold is the dominant variable cost in retail (over 80 percent of total costs), and the replacement cost is the marginal cost concept managers use in pricing decisions.

Replacement cost: The cost at which the retailer would replenish a unit of inventory at current prices, available at the store level in the scanner datasets. Distinct from average historical cost and used here as a direct proxy for marginal cost, eliminating one of the main sources of markup mismeasurement in prior empirical work.

Assortment composition: The set of products stocked and the expenditure weights of those products within a region. The paper’s central mechanism for regional markup variation — higher-income regions carry different (higher-quality, higher-margin) goods rather than charging different prices for the same goods.

Uniform pricing: The practice of charging identical prices for the same item across different geographic regions. Confirmed empirically in both the U.S. and Canadian scanner datasets, and embedded structurally in the theoretical model for the homogeneous good.

Active versus passive margin changes: A decomposition of gross margin changes into active changes (arising from retailer price decisions, irrespective of cost changes) and passive changes (arising when replacement cost changes but the retailer holds price fixed). Ninety-one percent of U.S. margin changes and 93 percent of Canadian changes are active.

Non-homothetic quadratic preferences: The utility specification (following Melitz and Ottaviano 2008) in which the absolute value of the own-price demand elasticity falls as quality consumption rises. This property implies that higher-quality goods carry higher markups and that richer regions, which demand higher quality, have higher average markups — the key mechanism linking income to markups in the model.

Hall approach to markup estimation: A production-function-based method in which the markup equals the output elasticity with respect to a variable input divided by that input’s cost share in revenue. The paper shows this yields estimates close to gross-margin estimates when implemented with true output quantities, but produces markups roughly 13-14 percent lower when revenue is substituted for output (a common approximation), confirming the Bond et al. 2020 bias.