Papers Containing Tag(s): 'National Science Foundation'
The following papers contain search terms that you selected. From the papers listed below, you can navigate to the PDF, the profile page for that working paper, or see all the working papers written by an author. You can also explore tags, keywords, and authors that occur frequently within these papers.
See Working Papers by Tag(s), Keywords(s), Author(s), or Search Text
Click here to search again
Frequently Occurring Concepts within this Search
Viewing papers 91 through 100 of 334
-
Working PaperThe Parental Gender Earnings Gap in the United States
January 2017
Working Paper Number:
CES-17-68
This paper examines the parental gender earnings gap, the within-couple differences in earnings over time, before and after the birth of a child. The presence and timing of children are important components of the gender wage gap, but there is selection in both decisions. We estimate the earnings gap between male and female spouses over time, which allows us to control for this timing choice as well as other shared external earnings shifters, such as the local labor market. We use Social Security Administration Detail Earnings Records (SSA-DER) data linked to the Survey of Income and Program Participation (SIPP) to examine a panel of earnings from 1978 to 2011 for the individuals in the SIPP sample. Our main results show that the spousal earnings gap doubles between two years before the birth of the first child and the year after that child is born. After the child's first year of life the gap continues to grow for the next five years, but at a much slower rate, then tapers off and even begins to fall once the child reaches school-age.View Full Paper PDF
-
Working PaperThe Need to Account for Complex Sampling Features when Analyzing Establishment Survey Data: An Illustration using the 2013 Business Research and Development and Innovation Survey (BRDIS)
January 2017
Working Paper Number:
CES-17-62
The importance of correctly accounting for complex sampling features when generating finite population inferences based on complex sample survey data sets has now been clearly established in a variety of fields, including those in both statistical and non statistical domains. Unfortunately, recent studies of analytic error have suggested that many secondary analysts of survey data do not ultimately account for these sampling features when analyzing their data, for a variety of possible reasons (e.g., poor documentation, or a data producer may not provide the information in a publicuse data set). The research in this area has focused exclusively on analyses of household survey data, and individual respondents. No research to date has considered how analysts are approaching the data collected in establishment surveys, and whether published articles advancing science based on analyses of establishment behaviors and outcomes are correctly accounting for complex sampling features. This article presents alternative analyses of real data from the 2013 Business Research and Development and Innovation Survey (BRDIS), and shows that a failure to account for the complex design features of the sample underlying these data can lead to substantial differences in inferences about the target population of establishments for the BRDIS.View Full Paper PDF
-
Working PaperRanking Firms Using Revealed Preference
January 2017
Working Paper Number:
CES-17-61
This paper estimates workers' preferences for firms by studying the structure of employer-toemployer transitions in U.S. administrative data. The paper uses a tool from numerical linear algebra to measure the central tendency of worker flows, which is closely related to the ranking of firms revealed by workers' choices. There is evidence for compensating differential when workers systematically move to lower-paying firms in a way that cannot be accounted for by layoffs or differences in recruiting intensity. The estimates suggest that compensating differentials account for over half of the firm component of the variance of earnings.View Full Paper PDF
-
Working PaperEffects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Improve the U.S. Statistical System?
January 2017
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.View Full Paper PDF
-
Working PaperEstimating the Local Productivity Spillovers from Science
January 2017
Working Paper Number:
CES-17-56
We estimate the local productivity spillovers from science by relating wages and real estate prices across metros to measures of scienti c activity in those metros. We address three fundamental challenges: (1) factor input adjustments using wages and real estate prices, along with Shepards Lemma, to estimate changes metros' productivity, which must equal changes in unit production cost; (2) unobserved differences in metros/causality using a share shift index that exploits historic variation in the mix of research in metros interacted with trends in federal funding for specific fields as an instrument; (3) unobserved differences in workers using data on the states in which people are born. Our estimates show a strong positive relationship between wages and scientifc research and a weak positive relationship for real estate prices. Overall, we estimate high rate of return to research.View Full Paper PDF
-
Working PaperHigh-Growth Entrepreneurship
January 2017
Working Paper Number:
CES-17-53
We study the patterns and determinants of job creation for a large cohort of start-up firms. Analysis of the universe of U.S. employers reveals strong persistence in employment size from firm birth to age seven, with a small fraction of firms accounting for most employment at both ages, patterns that are little explained by finely disaggregated industry controls or amount of finance. Linking to data from the Survey of Business Owners on characteristics of 54,700 founders of 36,400 start-ups, and defining 'high growth' as the top 5% of firms in the size distribution at age zero and seven, we find that women have a 30% lower probability of founding high-growth entrepreneurships at both ages. A similar gap for African-Americans at start-up disappears by age seven. Other differences with respect to race, ethnicity, and nativity are modest. Founder age is initially positively associated with high growth probability but the profile flattens after seven years and even becomes slightly negative. The education profile is initially concave, with advanced degree recipients no more likely to found high growth firms than high school graduates, but the former catch up to those with bachelor's degrees by firm age seven, while the latter do not. Most other relationships of high growth with founder characteristics are highly persistent over time. Prior business ownership is strongly positively associated, and veteran experience negatively associated, with high growth. A larger founding team raises the probability of high growth, while diversity (by gender, age, race/ethnicity, or nativity) either lowers the probability or has little effect. More start-up capital raises the high-growth propensity of firms founded by a sole proprietor, women, minorities, immigrants, veterans, novice entrepreneurs, and those who are younger or with less education. Perhaps surprisingly, women, minorities, and those with less education tend to choose high growth industries, but fewer of them achieve high growth compared to their industry peers.View Full Paper PDF
-
Working PaperFile Matching with Faulty Continuous Matching Variables
January 2017
Working Paper Number:
CES-17-45
We present LFCMV, a Bayesian file linking methodology designed to link records using continuous matching variables in situations where we do not expect values of these matching variables to agree exactly across matched pairs. The method involves a linking model for the distance between the matching variables of records in one file and the matching variables of their linked records in the second. This linking model is conditional on a vector indicating the links. We specify a mixture model for the distance component of the linking model, as this latent structure allows the distance between matching variables in linked pairs to vary across types of linked pairs. Finally, we specify a model for the linking vector. We describe the Gibbs sampling algorithm for sampling from the posterior distribution of this linkage model and use artificial data to illustrate model performance. We also introduce a linking application using public survey information and data from the U.S. Census of Manufactures and use LFCMV to link the records.View Full Paper PDF
-
Working PaperSorting Between and Within Industries: A Testable Model of Assortative Matching
January 2017
Working Paper Number:
CES-17-43
We test Shimer's (2005) theory of the sorting of workers between and within industrial sectors based on directed search with coordination frictions, deliberately maintaining its static general equilibrium framework. We fit the model to sector-specific wage, vacancy and output data, including publicly-available statistics that characterize the distribution of worker and employer wage heterogeneity across sectors. Our empirical method is general and can be applied to a broad class of assignment models. The results indicate that industries are the loci of sorting-more productive workers are employed in more productive industries. The evidence confirm that strong assortative matching can be present even when worker and employer components of wage heterogeneity are weakly correlated.View Full Paper PDF
-
Working PaperRevisiting the Economics of Privacy: Population Statistics and Confidentiality Protection as Public Goods
January 2017
Working Paper Number:
CES-17-37
We consider the problem of determining the optimal accuracy of public statistics when increased accuracy requires a loss of privacy. To formalize this allocation problem, we use tools from statistics and computer science to model the publication technology used by a public statistical agency. We derive the demand for accurate statistics from first principles to generate interdependent preferences that account for the public-good nature of both data accuracy and privacy loss. We first show data accuracy is inefficiently undersupplied by a private provider. Solving the appropriate social planner's problem produces an implementable publication strategy. We implement the socially optimal publication plan for statistics on income and health status using data from the American Community Survey, National Health Interview Survey, Federal Statistical System Public Opinion Survey and Cornell National Social Survey. Our analysis indicates that welfare losses from providing too much privacy protection and, therefore, too little accuracy can be substantial.View Full Paper PDF
-
Working PaperTwo Perspectives on Commuting: A Comparison of Home to Work Flows Across Job-Linked Survey and Administrative Files
January 2017
Working Paper Number:
CES-17-34
Commuting flows and workplace employment data have a wide constituency of users including urban and regional planners, social science and transportation researchers, and businesses. The U.S. Census Bureau releases two, national data products that give the magnitude and characteristics of home to work flows. The American Community Survey (ACS) tabulates households' responses on employment, workplace, and commuting behavior. The Longitudinal Employer-Household Dynamics (LEHD) program tabulates administrative records on jobs in the LEHD Origin-Destination Employment Statistics (LODES). Design differences across the datasets lead to divergence in a comparable statistic: county-to-county aggregate commute flows. To understand differences in the public use data, this study compares ACS and LEHD source files, using identifying information and probabilistic matching to join person and job records. In our assessment, we compare commuting statistics for job frames linked on person, employment status, employer, and workplace and we identify person and job characteristics as well as design features of the data frames that explain aggregate differences. We find a lower rate of within-county commuting and farther commutes in LODES. We attribute these greater distances to differences in workplace reporting and to uncertainty of establishment assignments in LEHD for workers at multi-unit employers. Minor contributing factors include differences in residence location and ACS workplace edits. The results of this analysis and the data infrastructure developed will support further work to understand and enhance commuting statistics in both datasets.View Full Paper PDF
