We investigate the impact of technology adoption on workers' wages and mobility in U.S. manufacturing plants by constructing and exploiting a unique Linked Employee-Employer data set containing longitudinal worker and plant information. We first examine the effect of technology use on wage determination, and find that technology adoption does not have a significant effect on high-skill workers, but negatively affects the earnings of low-skill workers after controlling for worker-plant fixed effects. This result seems to support the skill-biased technological change hypothesis. We next explore the impact of technology use on worker mobility, and find that mobility rates are higher in high-technology plants, and that high-skill workers are more mobile than their low and medium-skill counterparts. However, our technology-skill interaction term indicates that as the number of adopted technologies increases, the probability of exit of skilled workers decreases while that of unskilled workers increases.
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Technology Usage in U.S. Manufacturing Industries: New Evidence from the Survey of Manufacturing Technology
October 1991
Working Paper Number:
CES-91-07
Using a new dataset on technology usage in U.S. manufacturing plants, this paper describes how technology usage varies by plant and firm characteristics. The paper extends the previous literature in three important ways. First, it examines a wide range of relatively new technologies. Second, the paper uses a much larger and more representative set of firms and establishments than previous studies. Finally, the paper explores the role of firm R&D expenditures in the process of technology adoption. The main findings indicate that larger plants more readily use new technologies, plants owned by firms with high R&D-to-sales ratios adopt technologies more rapidly, and the relationship between plant age and technology usage is relatively weak.
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The Effect Of Technology Use On Productivity Growth
April 1996
Working Paper Number:
CES-96-02
This paper examines the relationship between the use of advanced technologies and productivity and productivity growth rates. We use data from the 1993 and 1988 Survey of Manufacturing Technology (SMT) to examine the use of advanced (computer based) technologies at two different points in time. We are also able to combine the survey data with the Longitudinal Research Database (LRD) to examine the relationships between plant performance, plant characteristics, and the use of advanced technologies. In addition, a subset of these plants were surveyed in both years, enabling us to directly associate changes in technology use with changes in plant productivity performance. The main findings of the study are as follows. First, diffusion is not the same across the surveyed technologies. Second, the adoption process is not smooth: plants added and dropped technologies over the six-year interval 1988-93. In fact, the average plant showed a gross change of roughly four technologies in achieving an average net increase of less than one new technology. In this regard, technology appears to be an experience good: plants experiment with particular technologies before deciding to add additional units or drop the technology entirely. We find that establishments that use advanced technologies exhibit higher productivity. This relationship is observed in both 1988 and 1993 even after accounting for other important factors associated with productivity: size, age, capital intensity, labor skill mix, and other controls for plant characteristics such as industry and region. In addition, the relationship between productivity and advanced technology use is observed both in the extent of technologies used and the intensity of their use. Finally, while there is some evidence that the use of advanced technologies is positively related to improved productivity performance, the data suggest that the dominant explanation for the observed cross-section relationship is that good performers are more likely to use advanced technologies than poorly performing operations.
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Locked In? The Enforceability of Covenants Not to Compete and the Careers of High-Tech Workers
January 2017
Working Paper Number:
CES-17-09
We examine how the enforceability of covenants not to compete (CNCs) affects employee mobility and wages of high-tech workers. We expect CNC enforceability to lengthen job spells and constrain mobility, but its impact on wages is ambiguous. Using a matched employer-employee dataset covering the universe of jobs in thirty U.S states, we find that higher CNC enforceability is associated with longer job spells (fewer jobs over time), and a greater chance of leaving the state for technology workers. Consistent with a 'lock-in' effect of CNCs, we find persistent wage-suppressing effects that last throughout a worker's job and employment history.
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Energy Intensity, Electricity Consumption, and Advanced Manufacturing Technology Usage
July 1993
Working Paper Number:
CES-93-09
This paper reports on the relationship between the usage of advanced manufacturing technologies (AMTs) and energy consumption patterns in manufacturing plants. Using data from the Survey of Manufacturing Technology and the 1987 Census of Manufactures, we model the energy intensity and the electricity intensity of plants as functions of AMT usage and plant age. The main findings are that plants which utilize AMTs are less energy intensive than plants not using AMTs but consume proportionately more electricity as a fuel source. Additionally, older plants are generally more energy intensive and rely on fossil fuels to a greater extent than younger plants.
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The Consequences of Long Term Unemployment:
Evidence from Matched Employer-Employee Data*
January 2016
Working Paper Number:
CES-16-40
It is well known that the long-term unemployed fare worse in the labor market than the short-term unemployed, but less clear why this is so. One potential explanation is that the long-term unemployed are 'bad apples' who had poorer prospects from the outset of their spells (heterogeneity). Another is that their bad outcomes are a consequence of the extended unemployment they have experienced (state dependence). We use Current Population Survey (CPS) data on unemployed individuals linked to wage records for the same people to distinguish between these competing explanations. For each person in our sample, we have wage record data that cover the period from 20 quarters before to 11 quarters after the quarter in which the person is observed in the CPS. This gives us rich information about prior and subsequent work histories not available to previous researchers that we use to control for individual heterogeneity that might be affecting subsequent labor market outcomes. Even with these controls in place, we find that unemployment duration has a strongly negative effect on the likelihood of subsequent employment. This finding is inconsistent with the heterogeneity ('bad apple') explanation for why the long-term unemployed fare worse than the short-term unemployed. We also find that longer unemployment durations are associated with lower subsequent earnings, though this is mainly attributable to the long-term unemployed having a lower likelihood of subsequent employment rather than to their having lower earnings once a job is found.
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Wages, Employer Size-Wage Premia and Employment Structure: Their Relationship to Advanced-Technology Usage at U.S. Manufacturing Establishments
December 1992
Working Paper Number:
CES-92-15
We study wages, size-wage premia and the employment structure (measured as the fraction of production workers in an establishment) and their relationship to the extent of advanced-technology usage at U.S, manufacturing plants. We begin by sketching a model of technology adoption based on Lucas (1978) that provides a framework for interpreting the data analysis. We then study a new Census Bureau survey of technology use at manufacturing plants. Workers in establishments that are classified as the most technology intensive earn a premium of 16 percent as compared to those in plants that are the least premium earned by workers in all but the very largest plants. The inclusion of the technology classification variables in standard wage regressions reduced the size-wage premia by as much as 60 percent for some size categories.
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Twisting the Demand Curve: Digitalization and the Older Workforce
November 2020
Working Paper Number:
CES-20-37
This paper uses U.S. Census Bureau panel data that link firm software investment to worker earnings. We regress the log of earnings of workers by age group on the software investment by their employing firm. To unpack the potential causal factors for differential software effects by age group we extend the AKM framework by including job-spell fixed effects that allow for a correlation between the worker-firm match and age and by including time-varying firm effects that allow for a correlation between wage-enhancing productivity shocks and software investments. Within job-spell, software capital raises earnings at a rate that declines post age 50 to about zero after age 65. By contrast, the effects of non-IT equipment investment on earnings increase for workers post age 50. The difference between the software and non-IT equipment effects suggests that our results are attributable to the technology rather than to age-related bargaining power. Our data further show that software capital increases the earnings of high-wage workers relative to low-wage workers and the earnings in high-wage firms relative to low-wage firms, and may thus widen earnings inequality within and across firms.
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How Workers Fare When Employers Innovate
May 2003
Working Paper Number:
CES-03-11
Complementing existing work on firm organizational structure and productivity, this paper examines the impact of organizational change on workers. We find evidence that employers do appear to compensate at least some of their workers for engaging in high performance workplace practices. We also find a significant association between high performance workplace practices and increased wage inequality. Finally, we examine the relationship between organizational structure and employment changes and find that some practices, such as self-managed teams, are associated with greater employment reductions, while other practices, such as the percentage of workers involved in job rotation, are associated with lower employment reductions.
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The Impact of Minimum Wages on Job Training: An Empirical Exploration with Establishment Data
February 2003
Working Paper Number:
CES-03-04
Human capital theory suggests that workers may finance on-the-job training by accepting lower wages during the training period. Minimum wage laws could reduce job training, then, to the extent they prevent low-wage workers from offering sufficient wage cuts to finance training. Empirical findings on the relationship between minimum wages and job training have failed to reach a consensus. Previous research has relied primarily on survey data from individual workers, which typically lack both detailed measures of job training and important information about the characteristics of firms. This study addresses the issue of minimum wages and on-the-job training with a unique employer survey. We find no evidence indicating that minimum wages reduce the average hours of training of trained employees, and little to suggest that minimum wages reduce the percentage of workers receiving training.
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Manufacturing Plants' Use of Temporary Workers: An Analysis Using Census Micro Data
December 2008
Working Paper Number:
CES-08-40
Using plant-level data from the Plant Capacity Utilization (PCU) Survey, we examine how manufacturing plants' use of temporary workers is associated with the nature of their output fluctuations and other plant characteristics. We find that plants tend to hire temporary workers when their output can be expected to fall, a result consistent with the notion that firms use temporary workers to reduce costs associated with dismissing permanent employees. In addition, we find that plants whose future output levels are subject to greater uncertainty tend to use more temporary workers. We also examine the effects of wage and benefit levels for permanent workers, unionization rates, turnover rates, seasonal factors, and plant size and age on the use of temporary workers; based on our results, we discuss various views of why firms use temporary workers.
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