We investigate the relationship between productivity growth and investment spikes using Census Bureau's plant-level data set for the U.S. food manufacturing industry. We find that productivity growth increases after investment spikes suggesting an efficiency gain or plants' learning effect. However, efficiency and the learning period associated with investment spikes differ among plants' productivity quartile ranks implying the differences in the plants' investment types such as expansionary, replacement or retooling. We find evidence of both convex and non-convex types of adjustment costs where lumpy plant-level investments suggest the possibility of non-convex adjustment costs and hazard estimation results suggest the possibility of convex adjustment costs. The downward sloping hazard can be due to the unobserved heterogeneity across plants such as plants' idiosyncratic obsolescence caused by different R&D capabilities and implies the existence of convex adjustment costs. Food plants frequently invest during their first few years of operation and high productivity plants postpone investing due to high fixed costs.
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Productivity Growth Patterns in U.S. Food Manufacturing: Case of Dairy Products Industry
May 2004
Working Paper Number:
CES-04-08
A panel constructed from the Census Bureau's Longitudinal Research Database is used to measure total factor productivity growth at the plant-level and analyzes the multifactor bias of technical change at three-digit product group level containing five different four-digit sub-group categories for the U.S. dairy products industry from 1972 through 1995. In the TFP growth decomposition, analyzing the growth and its components according to the quartile ranks show that scale effect is the most significant element of TFP growth except the plants in the third quartile rank where technical change dominates throughout the time periods. The exogenous input bias results show that throughout the time periods, technical change is 1) capital-using; 2) labor-using after 1980; 3) material-saving except 1981-1985 period; and, 4) energy-using except 1981-1985 and 1991-1995 periods. Plant productivity analysis indicate that less than 50% of the plants in the dairy products industry stay in the same category, indicating considerable movement between productivity rank categories. Investment analysis results indicate that plant-level investments are quite lumpy since a relatively small percent of observations account for a disproportionate share of overall investment. Productivity growth is found to be positively correlated with recent investment spikes for plants with TFP ranking in the middle two quartiles and uncorrelated with plants in the smallest and largest quartiles. Similarly, past TFP growth rates present no significant correlation with future investment spikes for plants in any quartile.
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Productivity Growth Patterns in U.S. Food Manufacturing: Case of Meat Products Industry
March 2004
Working Paper Number:
CES-04-04
A panel constructed from the Census Bureau's Longitudinal Research Database is used to measure total factor productivity growth at the plant-level and analyzes the multifactor bias of technical change for the U.S. meat products industry from 1972 through 1995. For example, addressing TFP growth decomposition for the meat products sub-sector by quartile ranks shows that the technical change effect is the dominant element of TFP growth for the first two quartiles, while the scale effect dominates TFP growth for the higher two quartiles. Throughout the time period, technical change is 1) capital-using; 2) material-saving; 3) labor-using; and, 4) energy-saving and becoming energy-using after 1980. The smaller sized plants are more likely to fluctuate in their productivity rankings; in contrast, large plants are more stable in their productivity rankings. Plant productivity analysis indicate that less than 50% of the plants in the meat industry stay in the same category, indicating considerable movement between productivity rank categories. Investment analysis results strongly indicate that plant-level investments are quite lumpy since a relatively small percent of observations account for a disproportionate share of overall investment. Productivity growth is found to be positively correlated with recent investment spikes for plants with TFP ranking in the middle two quartiles and uncorrelated with firms in the smallest and largest quartiles. Similarly, past TFP growth rates are positively correlated with future investment spikes for firms in the same quartiles. \
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The Missing Link: Technology, Productivity, and Investment
October 1995
Working Paper Number:
CES-95-12
This paper examines the relationship between productivity, investment, and age for over 14,000 plants in the U.S. manufacturing sector in the 1972-1988 period. Productivity patterns vary significantly due to plant heterogeneity. Productivity first increases and then decreases with respect to plant age, and size and industry are systematically correlated with productivity and productivity growth. However, there is virtually no observable relationship between investment and productivity or productivity growth. Overall, the results indicate that plant heterogeneity and fixed effects are more important determinants of observable productivity patterns than sunk costs or capital reallocation. Key Words: productivity, investment, technical change
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The Life Cycles of Industrial Plants
October 2001
Working Paper Number:
CES-01-10
The paper presents a dynamic programming model with multiple classes of capital goods to explain capital expenditures on existing plants over their lives. The empirical specification shows that the path of capital expenditures is explained by (a) complementarities between old and new capital goods, (b) the age of plants, (c) an index that captures the rate of technical change and (d) the labor intensiveness of a plant when it is newly born. The model is tested with Census data for roughly 6,000 manufacturing plants that were born after 1972.
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Estimating Capital Efficiency Schedules Within Production Functions
May 1992
Working Paper Number:
CES-92-04
The appropriate method for aggregating capital goods across vintages to produce a single capital stock measure has long been a contentious issue, and the literature covering this topic is quite extensive. This paper presents a methodology that estimates efficiency schedules within a production function, allowing the data to reveal how the efficiency of capital goods evolve as they age. Specifically we insert a parameterized investment stream into the position of a capital variable in a production function, and then estimate the parameters of the production function simultaneously with the parameters of the investment stream. Plant level panel data for a select group of steel plants employing a common technology are used to estimate the model. Our primary finding is that when using a simple Cobb Douglas production function, the estimated efficiency schedules appear to follow a geometric pattern, which is consistent with the estimates of economic depreciation of Hulten and Wykoff (1981). Results from more flexible functional forms produced much less precise and unreliable estimates.
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The Impact of Vintage and Survival on Productivity: Evidence from Cohorts of U.S. Manufacturing Plants
May 2000
Working Paper Number:
CES-00-06
This paper examines the evolution of productivity in U.S. manufacturing plants from 1963 to 1992. We define a 'vintage effect' as the change in productivity of recent cohorts of new plants relative to earlier cohorts of new plants, and a 'survival effect' as the change in productivity of a particular cohort of surviving plants as it ages. The data show that both factors contribute to industry productivity growth, but play offsetting roles in determining a cohort's relative position in the productivity distribution. Recent cohorts enter with significantly higher productivity than earlier entrants did, while surviving cohorts show significant increases in productivity as they age. These two effects roughly offset each other, however, so there is a rough convergence in productivity across cohorts in 1992 and 1987. (JEL Code: D24, L6)
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Job Reallocation And The Business Cycle: New Facts An Old Debate
September 1998
Working Paper Number:
CES-98-11
This paper provides new facts on the nature of job reallocation over the business cycle, and addresses the question of whether reallocation causes recessions or recessions cause reallocation. Although we do not resolve the question of causality, two general findings emerge that advance our understanding of job reallocation and business cycles. First, much of the cyclical fluctuation in gross job flows occurs in larger plants with relatively moderate employment growth that tends to be transitory, especially at medium-term horizons (up to five years). Unusually large employment growth rates, especially plant startups and shutdowns, are primarily small-plant phenomena and tend to be permanent, less cyclical, and occur later in recessions. Further, high job flow rates occur primarily in plants previously experiencing sharp employment contractions or expansions. Second, key variables that should determine the allocation factors of production across plants and sectors do in fact appear to be related to gross job flows, particularly job destruction. Relative prices, productivity, and investment exhibit time series correlations with job reallocation that suggest that allocative driving forces may contribute significantly to business cycle fluctuations.
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Why Are Plant Deaths Countercyclical: Reallocation Timing or Fragility?
November 2006
Working Paper Number:
CES-06-24
Because plant deaths destroy specific capital with large local economic impacts and potentially important macroeconmic effects, understanding the causes of deaths and, in particular, why they are concentrated in cyclical downturns, is important. The reallocationtiming hypothesis posits that plants suffering adverse permanent demand/productivity shocks delay shutdowns until cyclical downturns when plant capacity is less valuable, while the fragility hypothesis posits that shutdowns occur in downturns because the option value of maintaining the plant through low profitability periods is too small. I show that the effect that a plant's specific capital has on the timing of plant deaths differs across these two hypotheses and then use this insight to test the hypotheses' relative importance. I find that fragility is the dominant cause of the countercyclical behavior of plant deaths. This suggests that the endogenous destruction of capital is likely an important amplification and propagation mechanism for cyclical shocks and that stabilization policies have the benefit of reduced capital destruction.
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The Real Effects of Hedge Fund Activism: Productivity, Risk, and Product Market Competition
July 2012
Working Paper Number:
CES-12-14
This paper studies the long-term effect of hedge fund activism on the productivity of target firms using plant-level information from the U.S. Census Bureau. A typical target firm improves its production efficiency within two years after activism, and this improvement is concentrated in industries with a high degree of product market competition. By following plants that were sold post-intervention, we also find that efficient capital redeployment is an important channel via which activists create value. Furthermore, our analyses demonstrate that measuring performance using the Compustat data is likely to lead to a downward bias because target firms experiencing greater improvement post-intervention are also more likely to disappear from the Compustat database. Finally, consistent with recent work in asset-pricing linking firm investment decisions and expected returns, we show how changes to target firms' productivity are associated with a decline in systemic risk, particularly in competitive industries.
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An Option-Value Approach to Technology in U.S. Maufacturing: Evidence from Plant-Level Data
July 2000
Working Paper Number:
CES-00-12
Numerous empirical studies have examined the role of firm and industry heterogeneity in the decision to adopt new technologies using a Net Present Value framework. However, as suggested by the recently developed option-value theory, these studies may have overlooked the role of investment reversibility and uncertainty as important determinants of technology adoption. Using the option-value investment model as my underlying theoretical framework, I examine how these two factors affect the decision to adopt three advanced manufacturing technologies. My results support the option-value model's prediction that plants operating in industries facing higher investment reversibility and lower degrees of demand and technological uncertainty are more likely to adopt advanced manufacturing technologies.
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