Materials Prices and Productivity

Abstract

There is substantial within-industry variation in the prices that plants pay for their material inputs. Using plant-level data from the U.S. Census Bureau, I explore the consequences and sources of this variation in materials prices. For a sample of industries with relatively homogeneous products, the standard deviation of plant-level productivity would be 7% smaller if all plants faced the same materials prices. Moreover, plant-level materials prices are persistent, spatially correlated, and positively associated with the probability of exit. The contribution of entry and exit to aggregate productivity growth is smaller for productivity measures that are purged of materials price variation. After documenting these patterns, I discuss three potential sources of materials price variation: geography, di¤erences in suppliers'marginal costs, and within-supplier markup di¤erences. Together, these variables explain 15% of the variation of materials prices.

Production Function Estimation With Unobserved Input Price Dispersion

“…In all industries, the distributions of input prices are quite spread out, indicating that price dispersion is substantial. Our findings are partially corroborated by studies such as Ornaghi (2006) and Atalay (2014), which observe input prices directly and also find significant dispersion. Since our input prices are quality adjusted and identified through variation in firms expenditure ratios, they suggest that quality differences alone may not fully account for input price dispersion.…”

“…Our results indicate significant input price dispersion in all industries, providing further indication of the importance of controlling for unobserved price heterogeneity. The recovered distribution and evolution of intermediate prices are similar to that for studies in which input prices are directly observed (e.g., Atalay, 2014). We also find a positive correlation between intermediate input prices, wages, and productivity, also corroborating earlier studies (Kugler and Verhoogen, 2012).…”

Estimating Production Functions in Differentiated-Product Industries with Quantity Information and External Instruments

“…Focusing on eleven homogeneous products in the US Census of Manufactures, Foster et al (2008) estimate regressions with physical output quantities on the left-hand side, yielding output elasticities arguably purged of demand-side influences. Although Foster et al (2008) do not use physical quantities of inputs, in cases where inputs are homogeneous and quantities are observed it is straightforward to extend their approach and put physical quantities of inputs on the right-hand side (Atalay, 2014). But as suggested by Katayama et al (2009), Grieco and McDevitt (2016), Atkin et al (2019), Jaumandreu and Yin (2018) and others, using physical quantities may be misleading in differentiated-product industries where the quality and variety of outputs and inputs vary across firms and over time.…”

Input prices, productivity, and trade dynamics: long‐run effects of liberalization on Chinese paint manufacturers

“…The productivity literature has traditionally addressed the lack of input prices and quality by assuming that quality and prices are homogeneous within an industry (e.g., Levinsohn and Petrin, 2003). However, as shown in Ornaghi (2006) and Atalay (2014) using observed input price data, input prices can be very heterogeneous across firms and failure to control for this dispersion will bias estimates of the production function. A recent approach proposed by De Loecker et al ( 2016) employs a control function for unobserved input price variation that utilizes observed output prices and measures the productivity and markup effects of tariff changes.…”