Longitudinal Employer-Household Dynamics Data Research Articles

Reassessing the Spatial Mismatch Hypothesis

Using Longitudinal Employer-Household Dynamics data, we demonstrate several facts that are not consistent with the “spatial mismatch” hypothesis that residential segregation and uneven distribution of jobs limit Black workers' opportunities. We show that (a) there is no Black-White gap in the firm premium component of wages in an Abowd-Kramarz-Margolis wage decomposition; (b) there are both more jobs and more good jobs within commuting distance of Black than White workers; and (c) Black workers' commutes are shorter. We conclude that geographic proximity to good jobs is not a major source of racial earnings gaps in major US cities today.

Male Earnings Volatility in LEHD Before, During, and After the Great Recession

This article is part of a coordinated collection of papers on prime-age male earnings volatility. Each paper produces a similar set of statistics for the same reference population using a different primary data source. Our primary data source is the Census Bureau’s Longitudinal Employer-Household Dynamics (LEHD) infrastructure files. Using LEHD data from 1998 to 2016, we create a well-defined population frame to facilitate accurate estimation of temporal changes comparable to designed longitudinal samples of people. We show that earnings volatility, excluding increases during recessions, has declined over the analysis period, a finding robust to various sensitivity analyses.

A new measure of multiple jobholding in the U.S. economy

We create a measure of multiple jobholding from the U.S. Census Bureau's Longitudinal Employer-Household Dynamics data. This new series shows that 7.8 percent of persons in the U.S. are multiple jobholders, this percentage is pro-cyclical, and has been trending upward during the past several decades. The data also show that earnings from secondary jobs are, on average, 27.8 percent of a multiple jobholder's total quarterly earnings. Multiple jobholding occurs at all levels of earnings, with both higher- and lower-earnings multiple jobholders earning more than 25 percent of their total earnings from multiple jobs. In a regression analysis that controls for age, gender, and industry, multiple jobholders earn more at all jobs than do non-multiple jobholders. These new statistics tell us that multiple jobholding is more important in the U.S. economy than we knew.

Who’s ditching the bus?

This paper uses stop-level passenger count data in four cities to understand the nation-wide bus ridership decline between 2012 and 2018. The local characteristics associated with ridership change are evaluated in Portland, Miami, Minneapolis/St-Paul, and Atlanta. Poisson models explain ridership as a cross-section and the change thereof as a panel. While controlling for the change in frequency, jobs, and population, the correlation with local socio-demographic characteristics are investigated using data from the American Community Survey. The effect of changing neighborhood demographics on bus ridership are modeled using Longitudinal Employer-Household Dynamics data. At a point in time, neighborhoods with high proportions of non-white, carless, and most significantly, high-school-educated residents are the most likely to have high ridership. Over time, white neighborhoods are losing the most ridership across all four cities. In Miami and Atlanta, places with high concentrations of residents with college education and without access to a car also lose ridership at a faster rate. In Minneapolis/St-Paul, the proportion of college-educated residents is linked to ridership gain. The sign and significance of these results remain consistent even when controlling for intra-urban migration. Although bus ridership is declining across neighborhood characteristics, these results suggest that the underlying cause of bus ridership decline must be primarily affecting the travel behavior of white bus riders.

Do Unemployment Insurance Benefits Improve Match Quality? Evidence from Recent U.S. Recessions

We present new evidence on the impact of more generous unemployment insurance (UI) on workers’ ability to find jobs better suited to their skills. Using Longitudinal Employer-Household Dynamics data, we find the UI extensions introduced in the U.S. improved the quality of worker-job matches. Using Current Population Survey data, we also find that longer UI benefit durations decrease the mismatch between workers’ educational attainments and the educational requirements of jobs. We find bigger effects of UI on match quality for those more likely to be liquidity constrained—women, non-whites and less-educated workers—,suggesting UI extensions improve the functioning of the labor market.

Patterns of waste generation: A gradient boosting model for short-term waste prediction in New York City

Historical municipal solid waste (MSW) collection data supplied by the New York City Department of Sanitation (DSNY) was used in conjunction with other datasets related to New York City to forecast municipal solid waste generation across the city. Spatiotemporal tonnage data from the DSNY was combined with external data sets, including the Longitudinal Employer Household Dynamics data, the American Community Survey, the New York City Department of Finance’s Primary Land Use and Tax Lot Output data, and historical weather data to build a Gradient Boosting Regression Model. The model was trained on historical data from 2005 to 2011 and validation was performed both temporally and spatially. With this model, we are able to accurately (R2>0.88) forecast weekly MSW generation tonnages for each of the 232 geographic sections in NYC across three waste streams of refuse, paper and metal/glass/plastic. Importantly, the model identifies regularity of urban waste generation and is also able to capture very short timescale fluctuations associated to holidays, special events, seasonal variations, and weather related events. This research shows New York City’s waste generation trends and the importance of comprehensive data collection (especially weather patterns) in order to accurately predict waste generation.

Estimating work and home population using lidar-derived building volumes

ABSTRACTAs urban populations rapidly rise worldwide, it is increasingly necessary to determine the accurate distribution and configuration of the population in order to appropriate resources and services. Census-based methods for obtaining population counts are time consuming, labour intensive, and costly. Researchers have turned to remote sensing to estimate population from aerial and satellite datasets including lidar, which allows measures of building volume to be incorporated into population estimates. However, studies using lidar-derived building volumes have noted inconsistencies between population and building volume estimates in certain areas. In this article, we investigate this issue by incorporating both static and ambient population data into models using the US Census Bureau’s Longitudinal Employer-Household Dynamics (LEHD) database. To do this, we first develop a normalized home–work index to classify census blocks as primarily work-oriented, home-oriented, or mixed-use based on the LEHD data. We then employ ordinary least squares and geographically weighted regression (GWR) to explore the relationships between the different population groups (work, home, and mixed) and lidar-derived building volumes. We test these relationships across four diverse cities in Texas: Austin, Dallas, Houston, and San Antonio. Results suggest non-stationarity in the relationship between building volume and population with stronger, positive relationships in home-oriented and mixed-use blocks where the amount of building volume per person may be more consistent compared to work-oriented blocks. GWR models yielded high R2 values (0.9), particularly in mixed-use areas, indicating the potential for predictive relationships.

Validating RefUSA micro-data with the Longitudinal Employer-Household Dynamics Data

This paper validates the reliability of employment data in a frequently used establishment panel database assembled by InfoGroup by comparing it with employment and establishment data from the publicly available Longitudinal Employer-Household Dynamics (LEHD) and County Business Patterns (CBP) at the three-digit industry-by-state-by-year and two-digit industry-by-county-by-year levels between 1997 and 2013. We document substantial differences in both their cross-sectional and time series properties. Through an application involving the evaluation of the employment effects of state corporate tax rates, we also illustrate that the inclusion of fixed effects does not eliminate the bias associated with the extrapolation and/or other measurement error. These results suggest that both descriptive evidence and causal inference from the RefUSA data are unreliable.

Synthesizing spatial interaction data for social science research: Validation and an investigation of spatial mismatch in Wichita, Kansas

Rising economic segregation suggests a need to examine constraints to job access by race/ethnicity and economic inequality simultaneously. This often requires detailed socio-spatial interaction data to make progress on theoretical and modeling development, empirical studies and policy insights. Commuting data are commonly used because of its wide availability. Despite excellent work trip datasets from the U.S. Census such as the Census Transportation Planning Package and the Longitudinal Employer-Household Dynamics (LEHD) data, there are often gaps between the data that are available and ideal detailed commuting data suited to models and data analysis. This is because commuting data are available for a limited set of socio-economic dimensions and this coarseness limits researchers in their ability to uncover nuances of place-based generalizations about commuting, either socially or spatially. In one promising approach, an information minimizing technique was proposed as a workable practical method to synthesize disaggregated work trip flows. Because the strength of fit between predicted and observed trips is unknown, this paper validates this method using real commutes disaggregated by income and then synthesizes race–income work trips using LEHD data for the Wichita, Kansas metropolitan statistical area. We find that low-income Whites travel longer distances and have more dispersed travel patterns than all African-American and Asian income groups and that both low- and middle-income groups of all race groups have spatially constrained flows.

Designing Shuttle Connections to Commuter Rail with Census Origin and Destination Data

Designing shuttles that help transit riders complete the last mile of their trip (from a transit station or stop to their destination) plays an important role in leveraging rail capacity and growing a regional transit market. Unavailable or imprecise data have made it difficult to conduct effective design for shuttle routes on the basis of a defined potential market. This study developed a methodology for determining the size and location of the untapped market of commuters who drove long distances for work but could have made the same trip with commuter rail and last-mile shuttle service. The analysis was conducted with U.S. Census Longitudinal Employer–Household Dynamics data, a relatively new public data set of statewide workplace and residence pairs at the census block level. As a case study, the analysis was applied to the potential for last-mile shuttle connections between employment and 14 FrontRunner commuter rail stations outside downtown Salt Lake City, Utah. For the shuttle market to be captured, the workplace and residence pairs in this methodology were selected on the basis of three criteria: ( a) a residence within the FrontRunner park-and-ride shed, ( b) a workplace within 2 mi of a FrontRunner station, and ( c) an overall trip distance of 15 mi or greater. The study identified stations with the highest number of trips that met all three criteria and designed shuttle routes on the basis of the associated employment locations. Although this analysis focused on nontraditional commuting patterns, the same methodology would be useful for many other analyses of commuter markets.

Firm Age and Size in the Longitudinal Employer-Household Dynamics Data

The Census Bureau’s Quarterly Workforce Dynamics (QWI) and OnTheMap now provide detailed workforce statistics by employer age and size. These data allow a first look at the demographics of workers at small and young businesses as well as detailed analysis of how hiring, turnover, job creation/destruction vary throughout a firm’s lifespan. Both the QWI and OnTheMap are tabulated from the Longitudinal Employer-Household Dynamics (LEHD) linked employer-employee data. Firm age and size information was added to the LEHD data through integration of Business Dynamics Statistics (BDS) microdata into the LEHD jobs frame. This paper describes how these two new firm characteristics were added to the microdata and how they are tabulated in QWI and OnTheMap.

A Comparison of Person-Reported Industry to Employer-Reported Industry in Survey and Administrative Data

The Census Bureau collects industry information through surveys and administrative data and creates associated public-use statistics. In this paper, we compare person-reported industry in the American Community Survey (ACS) to employer-reported industry from the Quarterly Census of Employment and Wages (QCEW) that is part of the Census Bureau’s Longitudinal Employer-Household Dynamics (LEHD) program. This research provides necessary information on the use of administrative data as a supplement to survey data industry information, and the findings will be useful for anyone using industry information from either source. Our project is part of a larger effort to compare information on jobs from household survey data to employer-reported information. This research is the first to compare ACS job data to firm-based administrative data. We find an overall industry sector match rate of 75 percent, and a 61 percent match rate at the 4-digit Census Industry Code (CIC) level. Industry match rates vary by sector and by whether industry sector is classified using ACS or LEHD industry information. The educational services and health care and social assistance sectors have among the highest match rates. The management of companies and enterprises sector has the lowest match rate, using either ACS-reported or LEHD-reported sector. For individuals with imputed industry data, the industry sector match rate is only 14 percent. Our findings suggest that the industry distribution and the sample in a particular industry sector will differ depending on whether ACS or LEHD data are used.

Analyzing temporal changes in land-use–transportation relationships: A LEHD-based approach

Over the last decades there has been intense focus on better understanding land-use–transportation relationships. One prominent research area within this nexus has examined whether urban structural characteristics, such as the balance of jobs and housing, impacts broader transportation patterns, particularly commuting outcomes. Researchers have sought to quantify relationships in comparative studies across space and time, without fully realizing the latter due to sparse data availability, mainly due to reliance on government census type products which often lack adequate temporal resolution. This research utilizes a relatively new database from the U.S. Census known as Longitudinal Employer–Household Dynamics (LEHD) to explore temporal commuting–land-use relationships. This database has a high degree of spatial and temporal resolution, with worker information reported annually from 2002 to 2010. Locations of workers, jobs, and their commute flows are resolved to the census block scale. We analyze LEHD data over a 9-year period from 2002 to 2010 for a mid-sized urban area. Estimation of several commuting and jobs–housing metrics lends insights into growth and decline that have occurred in the recent housing boom and bust, and their broader implications for travel patterns.