Grab Longitudinal Employer-Household Dynamics (LEHD) Flat Files.

README

Jamaal Green, University of Pennsylvania; Dillon Mahmoudi, University of Maryland Baltimore County; Liming Wang, Portland State University

lehdr

Build Status

lehdr (pronounced: lee dur like a metric litre) is an R package that allows users to interface with the Longitudinal and Employer-Household Dynamics (LEHD) Origin-Destination Employment Statistics (LODES) dataset returned as dataframes. The package is continually in development and can be installed via CRAN.

Installation

You can install the released version of lehdr from CRAN with:

install.packages("lehdr")

And the development version from GitHub with:

install.packages("devtools", repos = "http://cran.us.r-project.org")
devtools::install_github("jamgreen/lehdr")

Usage

This first example pulls the Oregon (state = "or") 2020 (year = 2020) from LODES version 8 (version="LODES8", default), origin-destination (lodes_type = "od"), all jobs including private primary, secondary, and Federal (job_type = "JT01", default), all jobs across ages, earnings, and industry (segment = "S000", default), aggregated at the Census Tract level rather than the default Census Block (agg_geo = "tract").

library(lehdr)
or_od <- grab_lodes(state = "or", 
                    year = 2020, 
                    version = "LODES8", 
                    lodes_type = "od", 
                    job_type = "JT01",
                    segment = "S000", 
                    state_part = "main", 
                    agg_geo = "tract")

head(or_od)

The package can be used to retrieve multiple states and years at the same time by creating a vector or list. This second example pulls the Oregon AND Rhode Island (state = c("or", "ri")) for 2013 and 2014 (year = c(2013, 2014) or year = 2013:2014).

grab_lodes(state = c("or", "ri"), 
           year = c(2013, 2014), 
           lodes_type = "od", 
           job_type = "JT01", 
           segment = "S000", 
           state_part = "main", 
           agg_geo = "tract")           

Not all years are available for each state. To see all options for lodes_type, job_type, and segment and the availability for each state/year, please see the most recent LEHD Technical Document at https://lehd.ces.census.gov/data/lodes/LODES8/.

Using the optional version paramater, users can specify which LODES version to use. Version 8 is default (version="LODES8") is enumerated at 2020 Census blocks. LODES7 (version="LODES7") is enumerated at 2010 Census blocks, but ends in 2019. LODES5 (version="LODES5") is enumerated at 2000 Census blocks, but ends in 2009.

Other common uses might include retrieving Residential or Work Area Characteristics (lodes_type = "rac" or lodes_type = "wac" respectively), low income jobs (segment = "SE01") or good producing jobs (segment = "SI01"). Other common geographies might include retrieving data at the Census Block level (agg_geo = "block", not necessary as it is default).

Why lehdr?

The LODES dataset is frequently used by transportation and economic development planners, regional economists, disaster managers and other public servants in order to have a fine grained understanding of the distribution of employment. Such data is integral for regional travel demand models that help to dictate transportation policy options, regional economists and economic development planners interested in the spatial distribution of particular kinds of work use the data to weigh different industrial or workforce policy options. Finally, as a census product, the LODES data can be joined to census Decennial or American Community Survey data to help visualize the interactions between different population groups and work. In short, the LODES dataset is the only source of detailed geographic information on employment for the country and should be more widely available for researchers and analysts who work on regional development issues.

Future Development

Currently, lehdr is designed to grab the LODES flat files (origin-destination, workplace, and residential association files) and includes an option to aggregate results to the census tract level for analysts who find the fuzzing at the block level too great.

Next steps include exploring integration of the package with the sf and tigris packages to allow for easier mapping of LODES data.

Acknowledgements

This package would not exist in its current format without the inspiration of Bob Rudis’slodes package.