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Reallocation and Technology: Evidence From The U.S. Steel Industry
March 2013
Working Paper Number:
CES-13-06
We measure the impact of a drastic new technology for producing steel -- the minimill -- on the aggregate productivity of U.S. steel producers, using unique plant-level data between 1963 and 2002. We find that the sharp increase in the industry's productivity is linked to this new technology, and operates through two distinct mechanisms. First, minimills displaced the older technology, called vertically integrated production, and this reallocation of output was responsible for a third of the increase in the industry's productivity. Second, increased competition, due to the expansion of minimills, drove a substantial reallocation process within the group of vertically integrated producers, driving a resurgence in their productivity, and consequently of the industry's productivity as a whole.
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Are We Undercounting Reallocation's Contribution to Growth?
January 2013
Working Paper Number:
CES-13-55R
There has been a strong surge in aggregate productivity growth in India since 1990, following
significant economic reforms. Three recent studies have used two distinct methodologies to decompose the sources of growth, and all conclude that it has been driven by within-plant increases in technical efficiency and not between-plant reallocation of inputs. Given the nature of the reforms, where many barriers to input reallocation were removed, this finding has surprised researchers and been dubbed 'India's Mysterious Manufacturing Miracle.' In this paper, we show that the methodologies used may artificially understate the extent of reallocation. One approach, using growth in value added, counts all reallocation growth arising from the movement of intermediate inputs as technical efficiency growth. The second approach, using the Olley-Pakes decomposition, uses estimates of plant-level total factor productivity (TFP) as a proxy for the marginal product of inputs. However, in equilibrium, TFP and the marginal product of inputs are unrelated. Using microdata on manufacturing from five countries ' India, the U.S., Chile, Colombia, and Slovenia ' we show that both approaches significantly understate the true
role of reallocation in economic growth. In particular, reallocation of materials is responsible for over half of aggregate Indian manufacturing productivity growth since 2000, substantially larger than either the contribution of primary inputs or the change in the covariance of productivity and size.
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The Life Cycle of Plants in India and Mexico
September 2012
Working Paper Number:
CES-12-20
In the U.S., the average 40 year old plant employs almost eight times as many workers as the typical plant five years or younger. In contrast, surviving Indian plants exhibit little growth in terms of either employment or output. Mexico is intermediate to India and the U.S. in these respects: the average 40 year old Mexican plant employs twice as many workers as an average new plant. This pattern holds across many industries and for formal and informal establishments alike. The divergence in plant dynamics suggests lower investments by Indian and Mexican plants in process efficiency, quality, and in accessing markets at home and abroad. In simple GE models, we find that the difference in life cycle dynamics could lower aggregate manufacturing productivity on the order of 25% in India and Mexico relative to the U.S.
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Materials Prices and Productivity
June 2012
Working Paper Number:
CES-12-11
There is substantial within-industry variation, even within industries that use and produce homogeneous inputs and outputs, in the prices that plants pay for their material inputs. I explore, using plant-level data from the U.S. Census Bureau, 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 productivities would be 7% lower if all plants faced the same materials prices. Moreover, plant-level materials prices are both persistent across time and predictive of exit. The contribution of net entry to aggregate productivity growth is smaller for productivity measures that strip out di'erences in materials prices. After documenting these patterns, I discuss three potential sources of materials price variation: geography, di'erences in suppliers. marginal costs, and suppliers. price discriminatory behavior. Together, these variables account for 13% of the dispersion of materials prices. Finally, I demonstrate that plants.marginal costs are correlated with the marginal costs of their intermediate input suppliers.
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The Slow Growth of New Plants: Learning about Demand?
March 2012
Working Paper Number:
CES-12-06
It is well known that new businesses are typically much smaller than their established industry competitors, and that this size gap closes slowly. We show that even in commodity-like product markets, these patterns do not reflect productivity gaps, but rather differences in demand-side fundamentals. We document and explore patterns in plants' idiosyncratic demand levels by estimating a dynamic model of plant expansion in the presence of a demand accumulation process (e.g., building a customer base). We find active accumulation driven by plants' past production decisions quantitatively dominates passive demand accumulation, and that within-firm spillovers affect demand levels but not growth.
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Nature Versus Nurture in the Origins of Highly Productive Businesses: An Exploratory Analysis of U.S. Manufacturing Establishments
September 2011
Working Paper Number:
CES-11-26
This paper investigates the origins of productivity leaders, those that operate close to and help push out the production frontier. Do such businesses emerge as top performers from the very beginning of their lives, for example as the consequence of an outstanding founding idea, technology, or location? Or, at the other extreme, do they appear initially as completely average (or even underperformers) that exhibit gradual improvement as they learn and develop with age? To answer this question we draw upon five decades of U.S. Census of Manufacturing (CM) establishment-level data, tracing the productivity leaders of the most recent CM (2007) back over their observed life spans. We also examine possible industry-level correlates of variation in the extent of nature versus nurture that are suggested by theories of industry dynamics and economic growth.
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Productivity Dispersion and Plant Selection in the Ready-Mix Concrete Industry
September 2011
Working Paper Number:
CES-11-25
This paper presents a quantitative model of productivity dispersion to explain why inefficient producers are slowly selected out of the ready-mix concrete industry. Measured productivity dispersion between the 10th and 90th percentile falls from a 4 to 1 difference using OLS, to a 2 to 1 difference using a control function. Due to volatile productivity and high sunk entry costs, a dynamic oligopoly model shows that to rationalize small gaps in exit rates between high and low productivity plants, a plant in the top quintile must produce 1.5 times more than a plant in the bottom quintile.
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Using the Survey of Plant Capacity to Measure Capital Utilization
July 2011
Working Paper Number:
CES-11-19
Most capital in the United States is idle much of the time. By some measures, the average workweek of capital in U.S. manufacturing is as low as 55 hours per 168 hour week. The level and variability of capital utilization has important implications for understanding both the level of production and its cyclical fluctuations. This paper investigates a number of issues relating to aggregation of capital utilization measures from the Survey of Plant Capacity and makes recommendations on expanding and improving the published statistics deriving from the Survey of Plant Capacity. The paper documents a number of facts about properties of capital utilization. First, after growing for decades, capital utilization started to fall in mid 1990s. Second, capital utilization is a useful predictor of changes in capacity utilization and other factors of production. Third, adjustment of productivity measures for variable capital utilization improves statistical and economic properties of these measures. Fourth, the paper constructs weights to aggregate firm level capital utilization rates to industry and economy level, which is the major enhancement to available data.
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The Cyclicality of Productivity Dispersion
May 2011
Working Paper Number:
CES-11-15
Using plant-level data, I show that the dispersion of total factor productivity in U.S. durable manufacturing is greater in recessions than in booms. This cyclical property of productivity dispersion is much less pronounced in non-durable manufacturing. In durables, this phenomenon primarily reflects a relatively higher share of unproductive firms in a recession. In order to interpret these findings, I construct a business cycle model where production in durables requires a fixed input. In a boom, when the market price of this fixed input is high, only more productive firms enter and only more productive incumbents survive, which results in a more compressed productivity distribution. The resulting higher average productivity in durables endogenously translates into a lower average relative price of durables. Additionally, my model is consistent with the following business cycle facts: procyclical entry, procyclical aggregate total factor productivity, more procyclicality in durable than non-durable output, procyclical employment and countercyclicality in the relative price of durables and the cross section of stock returns.
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How Does Size Matter? Investigating the Relationships Among Plant Size, Industrial Structure, and Manufacturing Productivity
March 2011
Working Paper Number:
CES-11-08
Industrial concentration and market power have been studied extensively at the national scale, in fields ranging from economics and industrial organization to regional science and economic development. At the regional scale, however, industrial structure and firm size relationships have received little attention outside of non-generalizable case studies, primarily because accurate measurements require difficult-to-obtain plant- or firm-level information. Readily available secondary data sources on establishment size distributions (such as County Business Patterns or the Census of Manufactures) cannot be linked to performance information for particular establishments or firms. Yet region-specific industrial structure may be a crucial determinant of firm performance and thus regional economic fortunes as well (Chinitz 1961; Christopherson and Clark 2007). This paper examines how industrial concentration and agglomeration economies impact plant performance, focusing on the influence of establishment size in mediating these effects. The Longitudinal Research Database of the U.S. Census Bureau is accessed to construct production functions for three manufacturing industries nationwide. These production functions, specified at the establishment level, incorporate characteristics of establishments, industries, and regions, including spatially-differentiated measures of agglomeration economies. Establishment size is evaluated both as an absolute metric and relative to other regional industry plants, as theory suggests that absolute size may be most pertinent to agglomeration benefits but relative size more relevant to industrial structure (Caves and Barton 1990; Bothner 2005). The research builds on earlier work by the author that establishes a direct link between regional industry concentration and the productivity of manufacturing establishments.
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