Evaluating the Local Employment Dynamics Program as a Source of Journey-to- Work Data for Transportation Planning 1 Wende A. Mix, Ph.D. Associate Professor, Dept. of Geography and Planning, Buffalo State College Philip N. Fulton, Ph.D. Economic Research Services, USDA In 2003, the U.S. Census Bureau conducted a pilot project for the Bureau of Transportation Statistics to determine the feasibility of producing journey-to- work data from the Census Bureau's Local Employment Dynamics (LED) Program that could be used for transportation planning. The project resulted in several data files containing place of residence, place of work, wage, and industry classification data for the states of Illinois and Florida. Using the LED data for Illinois, this paper presents the results of an initial assessment of the comparability of LED journey-to-work data with data from the decennial census and the American Community Survey. Suggestions are made for next steps in the project and ways to improve the LED products to further enhance their potential utility for transportation planning. This paper presents an exploratory assessment of the comparability of LED journey-to-work data with data from the decennial census and the ACS. More specifically, the paper documents the results of comparisons of the 2001 LED work trip data for Illinois from the BTS project with data from the 2000 census and the 2001 ACS. The purpose of this analysis is to inform future decision making on the part of BTS and the Census Bureau regarding continued development of transportation planning data from the LED Program. With the transition from the census long-form to the ACS as the main source of journey- to-work data for use in transportation planning, the transportation planning community is actively engaged in understanding how data from the ACS can be integrated into their planning activities. In the context of this transition, now is an opportune time to also examine the utility of employment data from the LED Program as a possible component in an integrated transportation data system. [1][1] The views expressed in this paper are solely the authors’ and do not necessarily represent the views of the U.S. Bureau of the Census, the Bureau of Transportation Statistics, or the Illinois Department of Employment Security. Abstract Purpose of the Study Results of the Study Workers: In comparing estimates of total workers, the accuracy of the LED data can be affected by employees missing from the Unemployment Insurance wage records, employees whose residence address could not be obtained or geocoded, or the suppression of small blocks with few resident workers. Census and ACS data can be affected by the exclusion of second jobs, workers that were not "at work" during the reference week, and sampling error. But, all things being equal, we would expect the LED estimate of resident workers based on total jobs to be higher than the census or ACS estimates of total resident workers based on employed persons. Table 1 shows two LED estimates of the number of workers residing in Illinois derived by summing the records from the H-B Characteristics File of resident workers in each block and the O-D File of workers in block-to-block commuter flows. Counts of self-employed workers, unpaid family workers, and Federal government workers, available in standard ACS and census tabulations of labor force data, are subtracted from the worker total in order to more closely approximate the LED universe. Also, since the ACS and census estimates are for workers by place of residence, they include both workers who lived and worked in Illinois and workers who lived in Illinois but worked outside the state. The LED estimates only include workers who lived and worked in Illinois. Methods The basic question we seek to answer in this exploratory analysis is: How comparable are the journey-to-work data from the LED to the census and the ACS in terms of the number of workers, the distribution of worker flows, and the magnitude of those flows? (1) County Level Comparisons Total workers, Resident workers, Resident workers working inside and outside county of residence, Wage and earnings data (2) Tract Level Comparisons Total flows by geographic area type, Internal tract flows, Distance-decay estimation by area type, directional distribution of flows analysis for individual tracts. Only part of the completed analysis is included in this poster. Data SourceNumber of Workers 90-Percent Confidence Interval Lower Bound Upper Bound 2001 LED Home-Block (H-B) Characteristics File 5,150,511n/d 2001 LED Origin-Destination (O- D) Matrix File 4,301,766n/d 2000 Census5,203,0765,194,0095, ACS5,266,7435,194,2585,339, Census, adjusted to exclude workers who worked out-of-state 5,036,578n/d 2001 ACS, adjusted to exclude workers who worked out-of-state 5,087,674n/d Note: The ACS and census estimates are for the number of employed civilian persons 16 years and older residing in Illinois, excluding self-employed workers, unpaid family workers, and federal government workers which were subtracted from the total to more closely approximate the LED coverage universe. Since the LED estimates do not include workers that lived in Illinois and worked outside the state, the ACS and census estimates excluding out-of-state commuters were calculated to provide comparability. n/d = Not determinable Sources: 2001 ACS Supplementary Survey Summary Tables P068 and P Census Summary File 3 Tables P51 and P26. Table 1. Comparative Estimates of the Number of Workers Living in Illinois CountyACS 90-Percent Confidence Interval Census 90-Percent Confidence Interval LED ACS, excluding workers who work out-of- state Census, excluding workers who work out-of- state Lower Bound Upper Bound Lower Bound Upper Bound Cook2,206,950 2,169,319 2,244,5812,175,4542,167,0872,183,8212,242,3172,182,6742,151,524 DuPage429, , ,675425,123421,470428,776454,572426,563421,722 Kane201, , ,447177,590175,300179,880191,873200,628176,347 Lake280, , ,713269,354266,660272,048295,126274,268264,236 McHenry126, , ,554121,067119,300122,834132,039124,973119,372 Will244, , ,243223,437220,929225,945257,214240,670220,532 Madison106, , ,301111,017109,281112,75389,80472,01680,487 St. Clair98,700 91, ,11095,44793,80097,09481,87572,15067,958 Winnebago 114, , ,281122,746120,944124,548131,035109,302118,450 Note: The ACS and census estimates are for the number of employed civilian persons 16 years and older residing in each county, excluding self-employed workers, unpaid family workers, and federal government workers which are subtracted from the total to more closely approximate the LED coverage universe. Since the LED estimates do not include workers that lived in Illinois and worked outside the state, the ACS and census estimates excluding out-of-state commuters were calculated to provide comparability. Sources: 2001 ACS Supplementary Summary Table P068 and P Census Summary File 3 Tables P51 and P LED H-B File. Table 2. Comparative Estimates of the Number of Workers Living in Selected Illinois Counties The 2001 ACS included nine Illinois counties: six counties (Cook, DuPage, Kane, Lake, McHenry, and Will) in the Chicago-Naperville-Joliet, IL-IN-WI MSA, one county (Winnebago) in the Rockford, IL MSA, and two counties (Madison and St Clair) in the St. Louis, MO-IL MSA. Table 2 shows comparative estimates of the number of workers residing in each of these counties based on the LED, the census, and the ACS. The ACS and census estimates in the table again exclude self-employed workers, unpaid family workers, and Federal government workers in order to more closely approximate the LED universe. As with the state level data, the ACS and census estimates include workers who work outside Illinois, while the LED estimates do not. Again, alternative ACS and census estimates, adjusted to exclude out-of-state commuters were calculated using the percent of county workers found to be working outside Illinois in the ACS and the census. As expected, the LED estimates of the number of workers residing in each county are consistently higher than the ACS and census point estimates, with the exceptions of the ACS estimate for Kane County and both the census and ACS estimates for Madison and St. Clair counties. However, when the ACS and census estimates are adjusted to exclude out-of-state commuters to make them more comparable to the LED universe, the data for Madison and St. Clair fall in line with the general pattern. The anomaly with the Kane County ACS estimate stems from what appears to be an error in the ACS place-of-work data. The data show too few out-of-state commuters, so the adjustment is too small to bring the data into the proper relationship. The census data for Kane County show the more typical pattern.