Using data from a unique nationally representative sample of businesses, the Educational Quality of the Workforce National Employers Survey (EQW-NES), matched with the Bureau of the Census' Longitudinal Research Database (LRD), we examine the impact of workplace practices, information technology, and human capital investments on productivity. We estimate an augmented Cobb Douglas production function with both cross section and panel data covering the period of 1987-1993 using both within and GMM estimators. We find that what is associated with higher productivity is not so much whether or not an employer adopts a particular work practice but rather how that work practice is actually implemented within the establishment. We also find that those unionized establishments that have adopted what have been called new or "transformed" industrial relations practices that promote joint decision making coupled with incentive based compensation have higher productivty than other similar non-union plants, while those businesses that are unionized but maintain more traditional labor management relations have lower productivity. We also find that the higher the average educational level of production workers or the greater the proportion of non-managerial workers who use computers, the higher is plant productivity.
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Cheaper by the Dozen: Using Sibling Discounts at Catholic Schools to Estimate the Price Elasticity of Private School Attendance
October 2011
Working Paper Number:
CES-11-34
The effect of vouchers on sorting between private and public schools depends upon the price elasticity of demand for private schooling. Estimating this elasticity is empirically challenging because prices and quantities are jointly determined in the market for private schooling. We exploit a unique and previously undocumented source of variation in private school tuition to estimate this key parameter. A majority of Catholic elementary schools offer discounts to families that enroll more than one child in the school in a given year. Catholic school tuition costs therefore depend upon the interaction of the number and spacing of a family's children with the pricing policies of the local school. This within-neighborhood variation in tuition prices allows us to control for unobserved determinants of demand with a fine set of geographic fixed effects, while still identifying the price parameter. We use data from 3700 Catholic schools, matched to restricted Census data that identifies geography at the block level. We find that a standard deviation decrease in tuition prices increases the probability that a family will send its children to private school by one-half percentage point, which translates into an elasticity of Catholic school attendance with respect to tuition costs of -0.19. Our subgroup results suggest that a voucher program would disproportionately induce into private schools those who, along observable dimensions, are unlike those who currently attend private school.
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Estimating the Distribution of Plant-Level Manufacturing Energy Efficiency with Stochastic Frontier Regression
March 2007
Working Paper Number:
CES-07-07
A feature commonly used to distinguish between parametric/statistical models and engineering models is that engineering models explicitly represent best practice technologies while the parametric/statistical models are typically based on average practice. Measures of energy intensity based on average practice are less useful in the corporate management of energy or for public policy goal setting. In the context of company or plant level energy management, it is more useful to have a measure of energy intensity capable of representing where a company or plant lies within a distribution of performance. In other words, is the performance close (or far) from the industry best practice? This paper presents a parametric/statistical approach that can be used to measure best practice, thereby providing a measure of the difference, or 'efficiency gap' at a plant, company or overall industry level. The approach requires plant level data and applies a stochastic frontier regression analysis to energy use. Stochastic frontier regression analysis separates the energy intensity into three components, systematic effects, inefficiency, and statistical (random) error. The stochastic frontier can be viewed as a sub-vector input distance function. One advantage of this approach is that physical product mix can be included in the distance function, avoiding the problem of aggregating output to define a single energy/output ratio to measure energy intensity. The paper outlines the methods and gives an example of the analysis conducted for a non-public micro-dataset of wet corn refining plants.
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The Impact of Parental Resources on Human Capital Investment and Labor Market Outcomes: Evidence from the Great Recession
June 2024
Working Paper Number:
CES-24-34
I study the impact of parents' financial resources during adolescence on postsecondary human capital investment and labor market outcomes, using house value changes during the Great Recession of 2007-2009 as a natural experiment. I use several restricted-access datasets from the U.S. Census Bureau to create a novel dataset that includes intergenerational linkages between children and their parents. This data allows me to exploit house value variation within labor markets, addressing the identification concern that local house values are related to local economic conditions. I find that the average decrease to parents' home values lead to persistent decreases in bachelor's degree attainment of 1.26%, earnings of 1.96%, and full-time employment of 1.32%. Children of parents suffering larger house value shocks are more likely to substitute into two-year degree programs, drop out of college, or be enrolled in a college program in their late 20s.
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Wage and Productivity Dispersion in U.S. Manufacturing: The Role of Computer Investment
March 2000
Working Paper Number:
CES-00-01
By exploiting establishment-level data for U.S. manufacturing, this paper sheds new light on the source of the changes in the structure of production, wages, and employment that have occurred over the last several decades. Based on recent theoretical work by Caselli (1999) and Kremer and Maskin (1996), we focus on empirically investigating the following two hypotheses. The first hypothesis is that the channel through which skill biased technical change works through the economy is via changes in the dispersion in wages and productivity across establishments. The second is that the increased dispersion in wages and productivity across establishments is linked to differential rates of technological adoption across establishments. We find empirical support for these hypotheses. Our main findings are that (1) the between plant component of wage dispersion is an important and growing part of total wage dispersion, (2) much of the between plant increase in dispersion is within industries, (3) the between plant measures of wage and productivity dispersion have indreased substantially over the last few decades, (4) industries with large changes in between plant wage dispersion also exhibit large changes n between plant productivity dispersion, (5) a substantial fraction of the rising dispersion in wages and productivity is accounted for by increasing wage and productivity differentials across high and low computer investment per worker plants and high and low capital intensity plants, and (6) Changes in dispersion accounted for by such observable characteristics yield predicted industry level changes in wage and productivity dispersion that are highly correlated.
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The Classification of Manufacturing Industries: an Input-Based Clustering of Activity
August 1990
Working Paper Number:
CES-90-07
The classification and aggregation of manufacturing data is vital for the analysis and reporting of economic activity. Most organizations and researchers use the Standard Industrial Classification (SIC) system for this purpose. This is, however, not the only option. Our paper examines an alternative classification based on clustering activity using production technologies. While this approach yields results which are similar to the SIC, there are important differences between the two classifications in terms of the specific industrial categories and the amount of information lost through aggregation.
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Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Improve the U.S. Statistical System?
January 2017
Authors:
Lars Vilhuber,
John M. Abowd,
Daniel Weinberg,
Jerome P. Reiter,
Matthew D. Shapiro,
Robert F. Belli,
Noel Cressie,
David C. Folch,
Scott H. Holan,
Margaret C. Levenstein,
Kristen M. Olson,
Jolene Smyth,
Leen-Kiat Soh,
Bruce D. Spencer,
Seth E. Spielman,
Christopher K. Wikle
Working Paper Number:
CES-17-59R
The National Science Foundation-Census Bureau Research Network (NCRN) was established in 2011 to create interdisciplinary research nodes on methodological questions of interest and significance to the broader research community and to the Federal Statistical System (FSS), particularly the Census Bureau. The activities to date have covered both fundamental and applied statistical research and have focused at least in part on the training of current and future generations of researchers in skills of relevance to surveys and alternative measurement of economic units, households, and persons. This paper discusses some of the key research findings of the eight nodes, organized into six topics: (1) Improving census and survey data collection methods; (2) Using alternative sources of data; (3) Protecting privacy and confidentiality by improving disclosure avoidance; (4) Using spatial and spatio-temporal statistical modeling to improve estimates; (5) Assessing data cost and quality tradeoffs; and (6) Combining information from multiple sources. It also reports on collaborations across nodes and with federal agencies, new software developed, and educational activities and outcomes. The paper concludes with an evaluation of the ability of the FSS to apply the NCRN's research outcomes and suggests some next steps, as well as the implications of this research-network model for future federal government renewal initiatives.
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A Unified Framework for Measuring Preferences for Schools and Neighborhoods
October 2007
Working Paper Number:
CES-07-27
This paper develops a comprehensive framework for estimating household preferences for school and neighborhood attributes in the presence of sorting. It embeds a boundary discontinuity design in a heterogeneous model of residential choice to address the endogeneity of school and neighborhood attributes. The model is estimated using restricted-access Census data from a large metropolitan area, yielding a number of new results. First, households are willing to pay less than one percent more in house prices ' substantially lower than previous estimates ' when the average performance of the local school increases by five percent. Second, much of the apparent willingness to pay for more educated and wealthier neighbors is explained by the correlation of these sociodemographic measures with unobserved neighborhood quality. Third, neighborhood race is not capitalized directly into housing prices; instead, the negative correlation of neighborhood race and housing prices is due entirely to the fact that blacks live in unobservably lower quality neighborhoods. Finally, there is considerable heterogeneity in preferences for schools and neighbors: in particular, we find that households prefer to selfsegregate on the basis of both race and education.
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Resolving the Tension Between Access and Confidentiality: Past Experience and Future Plans at the U.S. Census Bureau
September 2009
Working Paper Number:
CES-09-33
This paper provides an historical context for access to U.S. Federal statistical data with a primary focus on the U.S. Census Bureau. We review the various modes used by the Census Bureau to make data available to users, and highlight the costs and benefits associated with each. We highlight some of the specific improvements underway or under consideration at the Census Bureau to better serve its data users, as well as discuss the broad strategies employed by statistical agencies to respond to the challenges of data access.
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Disconnected Geography: A Spatial Analysis of Disconnected Youth in the United States
January 2016
Working Paper Number:
CES-16-37
Since the Great Recession, US policy and advocacy groups have sought to better understand its effect on a group of especially vulnerable young adults who are not enrolled in school or training programs and not participating in the labor market, so called 'disconnected youth.' This article distinguishes between disconnected youth and unemployed youth and examines the spatial clustering of these two groups across counties in the US. The focus is to ascertain whether there are differences in underlying contextual factors among groups of counties that are mutually exclusive and spatially disparate (non-adjacent), comprising two types of spatial clusters ' high rates of disconnected youth and high rates of unemployed youth. Using restricted, household-level census data inside the Census Research Data Center (RDC) under special permission by the US Census Bureau, we were able to define these two groups using detailed household questionnaires that are not available to researchers outside the RDC. The geospatial patterns in the two types of clusters suggest that places with high concentrations of disconnected youth are distinctly different in terms of underlying characteristics from places with high concentrations of unemployed youth. These differences include, among other things, arrests for synthetic drug production, enclaves of poor in rural areas, persistent poverty in areas, educational attainment in the populace, children in poverty, persons without health insurance, the
social capital index, and elders who receive disability benefits. This article provides some preliminary evidence regarding the social forces underlying the two types of observed geospatial clusters and discusses how they differ.
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Assessing Multi-Dimensional Performance: Environmental and Economic Outcomes
May 2005
Working Paper Number:
CES-05-03
This study examines the determinants of environmental and economic performance for plants in three traditional smoke-stack industries: pulp and paper, oil, and steel. We combine data from Census Bureau and EPA databases and Compustat on the economic performance, regulatory activity and environmental performance on air and water pollution emissions and toxic releases. We find that plants with higher labor productivity tend to have lower emissions. Regulatory enforcement actions (but not inspections) are associated with lower emissions, and state-level political support for environmental issues is associated with lower water pollution and toxic releases. There is little evidence that plants owned by larger firms perform better, nor do older plants perform worse.
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