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Do Short-Term Incentives Affect Long-Term Productivity?
March 2020
Working Paper Number:
CES-20-10
Previous research shows that stock repurchases that are caused by earnings management lead to reductions in firm-level investment and employment. It is natural to expect firms to cut less productive investment and employment first, which could lead to a positive effect on firm-level productivity. However, using Census data, we find that firms make cuts across the board irrespective of plant productivity. This pattern seems to be associated with frictions in the labor market. Specifically, we find evidence that unionization of the labor force may prevent firms from doing efficient downsizing, forcing them to engage in easy or expedient downsizing instead. As a result of this inefficient downsizing, EPS-driven repurchases lead to a reduction in long-term productivity.
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Housing Booms and the U.S. Productivity Puzzle
January 2020
Working Paper Number:
CES-20-04
The United States has been experiencing a slowdown in productivity growth for more than a decade. I exploit geographic variation across U.S. Metropolitan Statistical Areas (MSAs) to investigate the link between the 2006-2012 decline in house prices (the housing bust) and the productivity slowdown. Instrumental variable estimates support a causal relationship between the housing bust and the productivity slowdown. The results imply that one standard deviation decline in house prices translates into an increment of the productivity gap -- i.e. how much an MSA would have to grow to catch up with the trend -- by 6.9p.p., where the average gap is 14.51%. Using a newly-constructed capital expenditures measure at the MSA level, I find that the long investment slump that came out of the Great Recession explains an important part of this effect. Next, I document that the housing bust led to the investment slump and, ultimately, the productivity slowdown, mostly through the collapse in consumption expenditures that followed the bust. Lastly, I construct a quantitative general equilibrium model that rationalizes these empirical findings, and find that the housing bust is behind roughly 50 percent of the productivity slowdown.
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Reconciling the Firm Size and Innovation Puzzle
March 2016
Working Paper Number:
CES-16-20RR
There is a prevailing view in both the academic literature and the popular press that firms need to behave more entrepreneurially. This view is reinforced by a stylized fact in the innovation literature that R&D productivity decreases with size. However, there is a second stylized fact in the innovation literature that R&D investment increases with size. Taken together, these stylized facts create a puzzle of seemingly irrational behavior by large firms--they are increasing spending despite decreasing returns. This paper is an effort to resolve that puzzle. We propose and test two alternative resolutions: 1) that it arises from mismeasurement of R&D productivity, and 2) that firm size endogenously drives R&D strategy, and that the returns to R&D strategies depend on scale. We are able to resolve the puzzle under the first tack--using a recent measure of R&D productivity, RQ, we find that both R&D spending and R&D productivity increase with scale. We had less success with the second tack--while firm size affects R&D strategy in the manners expected by theory, there is no strategy whose returns decrease in scale. Taken together, our results are consistent with the Schumpeter view that large firms are the major engine of growth, they both spend more in aggregate than small firms, and are more productive with that spending. Moreover the prescription that firms should behave more entrepreneurially, should be treated with caution--one small firm strategy has lower returns to scale than its large firm counterpart.
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Financial Frictions and Investment Dynamics in Multi-Plant Firms
October 2013
Working Paper Number:
CES-13-56
Using confidential Census data on U.S. manufacturing plants, we document that most of the dispersion in investment rates across plants occurs within rms instead of across firms. Between- firm dispersion is almost acyclical, but within- rm dispersion is strongly procyclical. To investigate the role of rms in the allocation of capital in the economy, we build a multi-plant model of the firm with frictions at both levels of aggregation. We show that external nancing constraints at the level of the rm can have important implications for plant-level investment dynamics. Finally, we present empirical evidence supporting the predictions of the model.
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Computer Networks and Productivity Revisited: Does Plant Size Matter? Evidence and Implications
September 2010
Working Paper Number:
CES-10-25
Numerous studies have documented a positive association between information technology (IT) investments and business- and establishment-level productivity, but these studies usually pay sole or disporportionate attention to small- or medium-sized entities. In this paper, we revisit the evidence for manufacturing plants presented in Atrostic and Nguyen (2005) and show that the positive relationship between computer networks and labor productivity is only found among small- and medium-sized plants. Indeed, for larger plants the relationship is negative, and employment-weighted estimates indicate computer networks have a negative relationship with the productivity of employees, on average. These findings indicate that computer network investments may have an ambiguous relationship with aggregate labor productivity growth.
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Linking Investment Spikes and Productivity Growth: U.S. Food Manufacturing Industry
October 2008
Working Paper Number:
CES-08-36
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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IT and Beyond: The Contribution of Heterogenous Capital to Productivity
December 2004
Working Paper Number:
CES-04-20
This paper explores the relationship between capital composition and productivity using a unique and remarkably detailed data set on firm-level, asset-specific investment in the U.S. Using cross-sectional and longitudinal regressions, I find that among all types of capital, only computers, communications equipment, software, and office building are associated (positively) with current and subsequent years' multifactor productivity. The link between offices and productivity, however, is shown to be due to the correlation between the use of offices and organizational capital. In contrast, the link between ICT equipment and productivity is robust to a number of controls and appears to be part causal effect and part reflection of the correlation between ICT and firm fixed (or slow-moving) effects. The implied marginal products by capital type are derived and compared to official data on rental prices; substantial differences exist for a number of key capital types. Lastly, I provide evidence of complementaries and substitutabilities among capital types ' a rejection of the common assumption of perfect substitutability ' and between particular capital types and labor.
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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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