Chapter 4.3 · Jobs here, workers there

Work, wages and access to jobs

In the fifth of counties with the fewest local jobs per job held by residents, 45.4% of the jobs held by residents were in the top earnings band of the 2019 LEHD Origin-Destination Employment Statistics against 39.5% of the jobs located in those counties, so what residents earn and what local employers pay describe different jobs.

Author: Vishal Singh
Affiliation: NYU Stern
Published: 9 October 2026

In the 2019 LEHD Origin-Destination Employment Statistics (LODES), 45.4% of the jobs placed at residents' homes were located in a different county (Alaska and Massachusetts left out). A survey of workers gives a lower share, 28% of workers traveling to a different county in the American Community Survey for 2009 to 2013.[1] LODES counts every job, second jobs included, and assigns the workplaces of employers with several sites by a model, so the two levels are not comparable. The jobs a county's residents hold are therefore not the jobs located in the county, and this chapter keeps the two apart. Unemployment is counted among the people who live in a county, pay is counted for the jobs located there, and the earnings that residents bring home come from jobs wherever they are.

In the fifth of counties with the fewest jobs located per job held by residents, 45.4% of the jobs held by residents were in the top earnings band, against 39.5% of the jobs located there. That fifth holds 6.3% of the jobs held by residents and 3.2% of the jobs located. Of its counties, 47.7% are metropolitan and 43.9% are nonmetropolitan with smaller towns or none. The fifth with the most jobs per resident job holds 46.0% and 56.5%, and 52.3% of its counties are metropolitan. Moving the county at a cut to the next fifth changes any of these shares by up to 0.1 points.

The data are counts for counties and tracts, so every claim describes places and not the workers inside them (ecological inference). The chapter shows who is counted in each source before it compares them, and it names the group, residents or jobs located, with every number.

What each source counts

Local Area Unemployment Statistics (LAUS) counts people by where they live. Its county figures are annual averages of monthly estimates that the Bureau of Labor Statistics builds from payroll counts, unemployment insurance claims and survey data and controls to state totals from a model.[2] The county file carries no margin of error, and the estimates for the smallest counties are the least certain. The Quarterly Census of Employment and Wages (QCEW) counts jobs by where they are located, from the wage reports of employers covered by state unemployment insurance. It leaves out the self-employed and some farm and domestic workers, counts part-time jobs as jobs, and includes bonuses in wages.[3] LODES places each job in three tables: at the worker's home, at the workplace, and as a pair of home county and work county.[4] LODES counts jobs, not people, so a worker with two jobs is counted once for each job; it adds noise to protect confidentiality, and it reports earnings only in three fixed bands of monthly earnings in current dollars.[5] The lowest band ends at $1,250 a month, and in this chapter a job is in the top band when it pays more than $3,333 a month. The cut does not move with prices, so shares of jobs in a band are compared within one year only.

The American Community Survey (ACS) supplies unemployment, labor force participation, commuting and work-from-home measures for residents of 2020 tracts. The Area Deprivation Index (ADI) ranks those tracts from least to most deprived.[6] The ADI is built partly from tract unemployment, income, the white-collar share of employed people and the share of households without a vehicle, so a gradient in unemployment, earnings, commuting or working from home against it is partly mechanical, and each tract figure below says so.

County unemployment rates moved together, and the order of county types reversed in 2020

Across the 3,143 counties and county equivalents in the LAUS file, the pooled unemployment rate of residents in 2023 was 3.6% (95% CI 3.3% to 3.9%), each county weighted by its labor force. Counted once each, counties ran from 2.1% at the fifth percentile to 5.6% at the ninety-fifth, around a median of 3.4% (Figure 1). A rate of 10% or more was published for 6 counties, and the two highest published estimates were for Imperial County, California (17.6%), and the Kusilvak Census Area, Alaska (14.8%). Estimates for small counties lean hardest on the model: 296 counties had a labor force under 2,000, and in a county that small a high rate can rest on a few hundred people. The map shows where residents live, wherever they work.

Figure 1

Unemployment among residents was 3.6% across counties in 2023, differed little between county types, and reached 10% or more in 6 counties

Unemployment rate, percent of the civilian labor force, annual average 2023, residents of each county, modeled estimates

Loading county values

Unemployment rate, annual average 2023 (percent of the labor force)

Values outside 2.1 to 5.6 take the end colors.

No data (3,143 counties)

Select a class to show only those places.

Find a county

Hover or tap a county for its value, or search by name. Zoom with the buttons on the map, then drag to move it.

Most counties sit toward the low end of the key, between 2.1% at the fifth percentile and 5.6% at the ninety-fifth,. The pooled rates of metropolitan counties (3.6%), nonmetropolitan counties with larger towns (3.7%) and those with smaller towns or none (3.8%) are close together. The map plots the rate for the people who live in each county, wherever they work; gray counties have no value.Note: LAUS county estimates are built from payroll counts, unemployment insurance claims and survey data and are controlled to state totals from a model; the county file carries no margin of error. 296 counties have a labor force below 2,000, and their estimates are the least certain.Note: LAUS codes Connecticut by planning region. The map draws the eight legacy counties, with the counts of unemployed residents and the labor force of each planning region spread over them in proportion to 2020 population, so the Connecticut values are approximate. Puerto Rico is not drawn; its 78 municipios are in the file.Note: The tooltip gives the labor force of each county.Source: Bureau of Labor Statistics, Local Area Unemployment Statistics, county annual averages, 2023; modeled estimates, not weighted

Unemployment differs less between county types than it moves over time. Grouping counties by the 2023 Rural-Urban Continuum Code gives three types: metropolitan counties, nonmetropolitan counties with larger towns (an urban population of 5,000 or more), and nonmetropolitan counties with smaller towns or none (Figure 2). In 2019 the pooled rates were 3.6% (95% CI 3.4% to 3.8%) in metropolitan counties, 4.1% (95% CI 3.8% to 4.3%) in the larger-town counties and 4.3% (95% CI 3.9% to 4.7%) in the smaller-town counties, gaps of 0.5 (95% CI 0.3 to 0.7) and 0.7 (95% CI 0.4 to 1.0) points below the nonmetropolitan counties. The metropolitan rate was below that of the smaller-town counties in each of the 20 years from 2000 to 2019, and the interval for the difference lies entirely below zero in 12 of them.

The order reversed in 2020, when the pooled rate of all counties rose from 3.7% (95% CI 3.5% to 3.8%) in 2019 to 8.1% (95% CI 7.4% to 8.7%). Metropolitan counties then had a rate of 8.2% (95% CI 7.5% to 8.8%), above the 7.3% (95% CI 6.9% to 7.6%) of the larger-town counties by 0.9 (95% CI 0.4 to 1.5) points and above the 6.7% (95% CI 6.2% to 7.3%) of the smaller-town counties by 1.5 (95% CI 0.7 to 2.2) points. By 2023 the three pooled rates were 3.6%, 3.7% and 3.8%, and the interval for the metropolitan minus smaller-town difference, −0.1 (95% CI −0.5 to 0.2) points, includes zero. The data describe the rates and do not say why the order reversed.

The intervals come from resampling states, so they show how much each pooled rate depends on which counties and states are in a group. They do not capture the model error of the county estimates, which the county file does not carry. No annual average is published for 7 Louisiana parishes in 2005 and 2006 (14 county-years), and the pooled rates of those years leave them out.

Figure 2

Metropolitan counties went from a lower unemployment rate than nonmetropolitan counties with smaller towns in 2019 (3.6% against 4.3%) to a higher one in 2020 (8.2% against 6.7%)

Unemployment rate, percent of the civilian labor force, pooled over the counties of each type, annual averages 2000 to 2024, residents of each county, modeled estimates

Unemployment rate (percent of the labor force)

Show data

Data for Metropolitan counties went from a lower unemployment rate than nonmetropolitan counties with smaller towns in 2019 (3.6% against 4.3%) to a higher one in 2020 (8.2% against 6.7%)
Year	County type	Pooled rate (percent)	Lower limit of the 95% interval	Upper limit of the 95% interval	Counties
2000	All counties	3.99	3.67	4.27	3,140
2000	Metropolitan	3.89	3.51	4.20	1,185
2000	Nonmetropolitan, larger towns	4.52	4.22	4.80	903
2000	Nonmetropolitan, smaller towns or none	4.81	4.43	5.23	1,052
2001	All counties	4.74	4.47	4.96	3,140
2001	Metropolitan	4.63	4.31	4.89	1,185
2001	Nonmetropolitan, larger towns	5.28	4.96	5.62	903
2001	Nonmetropolitan, smaller towns or none	5.54	5.09	6.00	1,052
2002	All counties	5.79	5.45	6.06	3,140
2002	Metropolitan	5.75	5.34	6.06	1,185
2002	Nonmetropolitan, larger towns	6.00	5.65	6.32	903
2002	Nonmetropolitan, smaller towns or none	6.16	5.70	6.65	1,052
2003	All counties	5.99	5.61	6.27	3,140
2003	Metropolitan	5.93	5.51	6.24	1,185
2003	Nonmetropolitan, larger towns	6.29	5.95	6.60	903
2003	Nonmetropolitan, smaller towns or none	6.42	5.96	6.87	1,052
2004	All counties	5.53	5.19	5.78	3,140
2004	Metropolitan	5.45	5.09	5.72	1,185
2004	Nonmetropolitan, larger towns	5.94	5.63	6.24	903
2004	Nonmetropolitan, smaller towns or none	6.03	5.61	6.47	1,052
2005	All counties	5.10	4.81	5.35	3,133
2005	Metropolitan	5.00	4.69	5.25	1,178
2005	Nonmetropolitan, larger towns	5.63	5.28	5.93	903
2005	Nonmetropolitan, smaller towns or none	5.83	5.38	6.29	1,052
2006	All counties	4.62	4.34	4.88	3,133
2006	Metropolitan	4.53	4.22	4.77	1,178
2006	Nonmetropolitan, larger towns	5.13	4.79	5.44	903
2006	Nonmetropolitan, smaller towns or none	5.35	4.89	5.83	1,052
2007	All counties	4.62	4.32	4.90	3,140
2007	Metropolitan	4.53	4.21	4.82	1,185
2007	Nonmetropolitan, larger towns	5.09	4.74	5.43	903
2007	Nonmetropolitan, smaller towns or none	5.34	4.84	5.84	1,052
2008	All counties	5.78	5.34	6.22	3,140
2008	Metropolitan	5.72	5.24	6.19	1,185
2008	Nonmetropolitan, larger towns	6.08	5.62	6.51	903
2008	Nonmetropolitan, smaller towns or none	6.26	5.65	6.87	1,052
2009	All counties	9.25	8.54	9.96	3,140
2009	Metropolitan	9.18	8.42	9.92	1,185
2009	Nonmetropolitan, larger towns	9.66	8.91	10.36	903
2009	Nonmetropolitan, smaller towns or none	9.62	8.65	10.55	1,052
2010	All counties	9.63	8.87	10.38	3,142
2010	Metropolitan	9.60	8.77	10.40	1,185
2010	Nonmetropolitan, larger towns	9.84	9.19	10.46	903
2010	Nonmetropolitan, smaller towns or none	9.79	8.83	10.69	1,054
2011	All counties	8.95	8.24	9.68	3,142
2011	Metropolitan	8.93	8.15	9.71	1,185
2011	Nonmetropolitan, larger towns	9.08	8.48	9.64	903
2011	Nonmetropolitan, smaller towns or none	9.08	8.23	9.88	1,054
2012	All counties	8.07	7.42	8.70	3,142
2012	Metropolitan	8.05	7.33	8.72	1,185
2012	Nonmetropolitan, larger towns	8.20	7.65	8.73	903
2012	Nonmetropolitan, smaller towns or none	8.19	7.39	8.95	1,054
2013	All counties	7.38	6.87	7.86	3,142
2013	Metropolitan	7.33	6.79	7.86	1,185
2013	Nonmetropolitan, larger towns	7.65	7.17	8.07	903
2013	Nonmetropolitan, smaller towns or none	7.77	7.06	8.47	1,054
2014	All counties	6.17	5.76	6.58	3,142
2014	Metropolitan	6.13	5.69	6.55	1,185
2014	Nonmetropolitan, larger towns	6.37	5.99	6.74	903
2014	Nonmetropolitan, smaller towns or none	6.59	5.99	7.20	1,054
2015	All counties	5.28	4.97	5.57	3,142
2015	Metropolitan	5.22	4.88	5.53	1,185
2015	Nonmetropolitan, larger towns	5.61	5.28	5.94	903
2015	Nonmetropolitan, smaller towns or none	5.85	5.36	6.35	1,054
2016	All counties	4.87	4.62	5.08	3,142
2016	Metropolitan	4.79	4.51	5.02	1,185
2016	Nonmetropolitan, larger towns	5.36	5.03	5.64	903
2016	Nonmetropolitan, smaller towns or none	5.67	5.25	6.09	1,054
2017	All counties	4.36	4.13	4.53	3,142
2017	Metropolitan	4.30	4.07	4.48	1,185
2017	Nonmetropolitan, larger towns	4.69	4.38	4.96	903
2017	Nonmetropolitan, smaller towns or none	4.92	4.54	5.30	1,054
2018	All counties	3.90	3.71	4.05	3,142
2018	Metropolitan	3.84	3.65	3.99	1,185
2018	Nonmetropolitan, larger towns	4.21	3.95	4.45	903
2018	Nonmetropolitan, smaller towns or none	4.39	4.05	4.75	1,054
2019	All counties	3.67	3.47	3.84	3,142
2019	Metropolitan	3.60	3.39	3.78	1,185
2019	Nonmetropolitan, larger towns	4.06	3.81	4.31	903
2019	Nonmetropolitan, smaller towns or none	4.29	3.94	4.66	1,054
2020	All counties	8.07	7.42	8.66	3,143
2020	Metropolitan	8.21	7.51	8.81	1,185
2020	Nonmetropolitan, larger towns	7.26	6.86	7.63	903
2020	Nonmetropolitan, smaller towns or none	6.74	6.19	7.28	1,055
2021	All counties	5.36	4.78	5.88	3,143
2021	Metropolitan	5.43	4.80	5.98	1,185
2021	Nonmetropolitan, larger towns	4.88	4.52	5.24	903
2021	Nonmetropolitan, smaller towns or none	4.69	4.33	5.07	1,055
2022	All counties	3.65	3.39	3.87	3,143
2022	Metropolitan	3.64	3.35	3.87	1,185
2022	Nonmetropolitan, larger towns	3.73	3.47	3.97	903
2022	Nonmetropolitan, smaller towns or none	3.75	3.46	4.06	1,055
2023	All counties	3.64	3.33	3.92	3,143
2023	Metropolitan	3.63	3.28	3.94	1,185
2023	Nonmetropolitan, larger towns	3.66	3.43	3.88	903
2023	Nonmetropolitan, smaller towns or none	3.75	3.48	4.03	1,055
2024	All counties	4.02	3.69	4.35	3,143
2024	Metropolitan	4.03	3.67	4.38	1,185
2024	Nonmetropolitan, larger towns	3.98	3.75	4.22	903
2024	Nonmetropolitan, smaller towns or none	4.07	3.77	4.40	1,055

The lines move together, rising in 2009 and 2010 and again in 2020, and the order of the three county types reverses between 2019 and 2020. By 2022 the gaps between types are within their intervals. Each line pools unemployed residents over labor force for the counties of one type, the dashed line pools all counties, and the bands, drawn for two of the lines, are 95% intervals from a bootstrap over states.Note: A county type follows the 2023 Rural-Urban Continuum Code of the county: codes 1 to 3 are metropolitan, 4 to 7 are nonmetropolitan with an urban population of 5,000 or more, and 8 and 9 are nonmetropolitan with an urban population under 5,000. One classification is applied to every year.Note: The intervals reflect which counties and states are in each group, not the model error of the county estimates, which LAUS does not publish. The tooltips give the interval for every line.Note: No annual average is published for 7 Louisiana parishes in 2005 and 2006, which leaves them out of the pooled rates of those two years. Puerto Rico is not included.Source: Bureau of Labor Statistics, Local Area Unemployment Statistics, county annual averages, 2000-2024; modeled estimates, pooled with labor-force weights; county types from the USDA 2023 Rural-Urban Continuum Codes

What employers in a county pay

QCEW reports total wages and average employment for the jobs located in each county. In 2023, 3,141 counties had a published average annual pay per job, and together they held 148.0 million covered jobs. Counted once each, counties ran from $39,160 at the fifth percentile to $72,089 at the ninety-fifth, around a median of $49,668, and weighted by jobs the mean was $71,075 (Figure 3). The mean sits near the metropolitan figure because metropolitan counties hold 88.9% of the covered jobs. Jobs located in metropolitan counties paid $73,585 on average, against $51,618 in nonmetropolitan counties with larger towns and $48,786 in those with smaller towns or none. Santa Clara County and San Mateo County, California, had the two highest county averages ($174,694 and $174,319), and Worth County, Missouri, with 385 jobs, had the lowest ($29,704).

Every one of these figures is the pay of jobs located in a county. It is not the earnings of the people who live there, who may work in another county, and the next section measures how far the two differ. The income of residents, as tax returns report it, is in Chapter 4.1, Home value, income and redlining.

The Bureau withholds the totals of 1 county in 2023, Eureka County, Nevada, to protect an employer's confidentiality, and the map draws it gray. The county-by-sector detail is withheld far more often: in 2023 the Bureau withheld 25% of the cells for private employers, 32% for local governments and 29% for state governments. A withheld cell is missing and not zero, and the sector cells cannot be summed to rebuild a county's private pay, so this chapter uses the county totals only.

Figure 3

Jobs located in metropolitan counties paid $73,585 a year on average in 2023, against $48,786 in nonmetropolitan counties with smaller towns or none

Average annual pay per job, current dollars, for jobs located in each county and covered by unemployment insurance, 2023, jobs at the workplace

Loading county values

Average annual pay per job at the workplace, 2023 (current dollars)

Values outside $39,000 to $72,000 take the end colors.

No data (3,143 counties)

Select a class to show only those places.

Find a county

Hover or tap a county for its value, or search by name. Zoom with the buttons on the map, then drag to move it.

Counties toward the high end of the key are mostly metropolitan counties, and most counties sit toward the low end, with a median of $49,668 and a fifth to ninety-fifth percentile range of $39,160 to $72,089. The map plots pay for the jobs located in each county, not the earnings of the people who live there; gray counties have no value.Note: Pay is total wages divided by average employment for jobs covered by state unemployment insurance. It mixes full-time and part-time jobs, includes bonuses, and leaves out the self-employed and some farm and domestic workers.Note: Of the 2 gray counties, one has totals withheld by the Bureau to protect an employer's confidentiality and one is not in the file. A withheld cell is missing, never zero.Note: QCEW codes Connecticut by its legacy counties through 2023, the counties drawn here. Puerto Rico is not drawn. Dollars are current, so this map compares counties within 2023 and not across years.Note: Totals are full counts from employer reports, so they carry no sampling interval.Source: Bureau of Labor Statistics, Quarterly Census of Employment and Wages, county annual averages, 2023; full counts of covered payroll

Residents and workplaces are different groups of jobs

Commuting across county lines is common. Of the 142.0 million jobs in the 2019 LODES flows, 45.4% (95% CI 41.8% to 49.1%) were held by residents of one county and located in another. The share rises as counties get smaller (Figure 4). It was 44.1% (95% CI 40.3% to 48.3%) for residents of metropolitan counties, 51.5% (95% CI 49.1% to 53.4%) for the nonmetropolitan counties with larger towns and 62.4% (95% CI 59.5% to 64.8%) for those with smaller towns or none. The larger-town counties exceed the metropolitan counties by 7.4 (95% CI 2.7 to 10.9) points, the smaller-town counties exceed the larger-town counties by 11.0 (95% CI 9.0 to 12.9) points, and the smaller-town counties exceed the metropolitan counties by 18.4 (95% CI 13.7 to 22.0) points.

The share rose between 2010 and 2019 in each type, over the same states in every year. Across all counties it went from 44.1% to 45.4%, a rise of 1.3 (95% CI 0.9 to 1.6) points. In metropolitan counties it went from 43.0% to 44.1% (1.1 (95% CI 0.8 to 1.5) points), in larger-town counties from 48.6% to 51.5% (2.9 (95% CI 2.4 to 3.3) points) and in smaller-town counties from 59.5% to 62.4% (3.0 (95% CI 2.5 to 3.4) points).

Figure 4

In 2019, 62.4% of the jobs held by residents of nonmetropolitan counties with smaller towns or none were in another county, against 44.1% for residents of metropolitan counties

Percent of jobs held by a county's residents that are located in a different county, pooled over the counties of each type, 2010, 2015 and 2019, jobs counted by home county

Jobs held by residents that are located in another county (percent)

Show data

Data for In 2019, 62.4% of the jobs held by residents of nonmetropolitan counties with smaller towns or none were in another county, against 44.1% for residents of metropolitan counties
conf_high	conf_low	estimate	jobs	n_counties	series	year
46.89	39.37	42.95	105160058	1,171	Metropolitan	2010
50.45	46.30	48.58	13636739	894	Nonmetropolitan, larger towns	2010
61.81	56.61	59.46	4091694	1,034	Nonmetropolitan, smaller towns or none	2010
47.03	39.24	42.87	115867165	1,171	Metropolitan	2015
51.16	46.90	49.24	14121233	894	Nonmetropolitan, larger towns	2015
62.95	57.67	60.51	4083810	1,034	Nonmetropolitan, smaller towns or none	2015
48.26	40.33	44.09	123293844	1,171	Metropolitan	2019
53.45	49.13	51.46	14490434	894	Nonmetropolitan, larger towns	2019
64.84	59.55	62.44	4179253	1,034	Nonmetropolitan, smaller towns or none	2019

The line for the nonmetropolitan counties with smaller towns or none is highest and the metropolitan line lowest in every year shown, and each line rises between 2010 and 2019. Each point is the pooled share of resident jobs located outside the home county, with a 95% interval from a bootstrap over states.Note: Alaska and Massachusetts are left out of every year, so every year covers the same states. Alaska has no workplace file for 2019 and its residence table holds 3,090 jobs, against 308,964 in 2015. The Massachusetts workplace file for 2010 is a stub of 2 tracts and 17 jobs, and its residence table holds 151,718 jobs, against 3,330,566 in 2015. Puerto Rico is not in the LODES tables used here.Note: LODES counts every job, second jobs included, and assigns the workplaces of employers with several sites by a model. A survey of workers gives a lower share (28% of workers traveled to a different county in the 2009-2013 ACS), so the two levels are not comparable.Source: LEHD LODES origin-destination tables, 2010, 2015 and 2019 (Alaska and Massachusetts left out); jobs-weighted counts with confidentiality noise; county types from the USDA 2023 Rural-Urban Continuum Codes

Commuting separates the jobs located in a county from the jobs its residents hold, and the two differ in what they pay. LODES reports both in the same earnings bands, so the share of jobs in the top band can be compared directly. Across 3,099 counties, ranked by jobs located per job held by residents and cut into fifths, the fifth with the fewest jobs located per resident job (at most 0.60) had a top-band share of 45.4% (95% CI 43.4% to 47.3%) among jobs held by residents and 39.5% (95% CI 36.7% to 42.1%) among jobs located there, a difference of 5.9 (95% CI 3.2 to 7.8) points (Figure 5). The order reversed in the fifth with the most jobs per resident job (at least 1.01), where 47.8% (95% CI 46.0% to 49.3%) of residents' jobs were in the top band against 49.8% (95% CI 47.8% to 51.5%) of jobs located, a difference of 2.0 (95% CI 1.2 to 2.9) points in the other direction.

Figure 5

In the fifth of counties with the fewest jobs per resident job, 45.4% of jobs held by residents were in the top earnings band against 39.5% of the jobs located there

Percent of jobs paying more than $3,333 a month, jobs held by a county's residents against jobs located in the county, fifths of counties ranked by jobs located per job held by residents, 2019

Jobs located in the county
Jobs held by the county's residents

Show data

Data for In the fifth of counties with the fewest jobs per resident job, 45.4% of jobs held by residents were in the top earnings band against 39.5% of the jobs located there
Fifth of counties	Counties	Jobs located per job held by residents, lowest	Jobs located per job held by residents, highest	Top-band share, jobs located (percent)	Top-band share, jobs held by residents (percent)	Jobs held by residents	Jobs located
Fewest jobs per resident job	619	0.14	0.60	39.5	45.4	8,939,569	4,609,391
Second fifth	620	0.60	0.74	41.6	46.4	15,095,852	10,201,971
Middle fifth	620	0.74	0.86	43.0	46.7	20,101,609	16,046,376
Fourth fifth	620	0.86	1.01	45.3	46.9	32,659,196	30,931,375
Most jobs per resident job	620	1.01	11.24	49.8	47.8	65,464,789	80,367,572

In every fifth but the last, the dot for residents' jobs lies to the right of the dot for jobs located, and in the fifth with the most jobs per resident job it lies to the left. Each dot is a pooled share with a 95% interval from a bootstrap over states, and the last column gives the gap, residents' jobs minus jobs located.Note: The same jobs are counted in both tables, once at the home and once at the workplace, so the two shares agree when every county is pooled; they differ in a group of counties because of commuting across the group's boundary.Note: Counties are ranked on jobs located per job held by residents, a count ratio that does not use earnings, and the fifths hold near-equal numbers of counties, not equal numbers of jobs. The lowest fifth holds 6.3% of jobs held by residents and 3.2% of jobs located, and the highest fifth holds 46.0% and 56.5%. Moving the county at a cut to the next fifth changes any of these shares by up to 0.1 points. The ranges of the ratio are in the table. Earnings bands are fixed in current dollars, so shares are compared within 2019 only.Note: Alaska (no 2019 workplace file) and Massachusetts (a stub 2010 workplace file) are left out of every LODES figure, so the same states are covered in every year.Source: LEHD LODES residence and workplace tables, 2019 (Alaska and Massachusetts left out); jobs-weighted counts with confidentiality noise, earnings bands in current dollars

Jobs located vary more across these fifths than jobs held by residents do. From the lowest to the highest fifth the top-band share of jobs located rises by 10.3 (95% CI 8.5 to 12.5) points and the share among jobs held by residents by 2.4 (95% CI 0.5 to 4.5) points, a difference of 7.9 (95% CI 5.7 to 9.7) points. The jobs that residents hold differ less across places than the jobs located in those places.

County by county, the top-band share of jobs held by residents exceeded that of jobs located in 66.0% (95% CI 63.8% to 68.3%) of the 2,877 counties with at least 1,000 jobs in each table (Figure 6). Noise in the small-county shares pulls dots apart in both directions, so the share above the diagonal is the figure to read, not the slope of the cloud.

Figure 6

In 66.0% of counties the jobs held by residents were more often in the top earnings band than the jobs located in the county

Percent of jobs paying more than $3,333 a month, jobs held by a county's residents against jobs located in the county, one dot per county, 2019

Loading data

Dots above the diagonal are counties where residents' jobs are in the top band more often than the jobs located there, and most dots lie above it. Each dot is one of 2,877 counties with at least 1,000 jobs in each table, sized by jobs held by residents and colored by county type.Note: Counties with fewer than 1,000 jobs in either table are not drawn because noise added for confidentiality makes their shares unreliable; they are in every other figure of the chapter. Alaska and Massachusetts are left out.Note: Both axes use the same earnings band in current dollars for the same year, so the comparison is between populations (jobs at the home and jobs at the workplace) and not between bands. The tooltip gives the county and the jobs held by residents. Noise in county shares pulls dots apart in both directions, so the share above the diagonal is the figure to read and not the slope of the cloud.Source: LEHD LODES residence and workplace tables, 2019 (Alaska and Massachusetts left out); full counts of jobs with confidentiality noise, earnings bands in current dollars

QCEW pay describes jobs located, and it tracks the workplace measure more closely than the residence measure. Across the counties with a published average pay in 2019, weighted by 2020 population, pay correlates +0.87 (95% CI 0.86 to 0.92) with the top-band share of jobs located and +0.73 (95% CI 0.69 to 0.77) with the top-band share of jobs held by residents, a difference of +0.15 (95% CI 0.11 to 0.21). That is consistent with the pay in a county describing its jobs and not its residents' earnings. Part of the contrast may come from a shared source, since QCEW and the LODES workplace table both draw on state unemployment insurance wage records. The data give residents' earnings only as three bands, so they cannot say how many dollars residents bring home. How earnings and income move across generations is the subject of Chapter 3.3, Race, income and mobility.

Residents and jobs in neighborhoods of different deprivation

The ACS and the residence table of LODES describe the same tracts, and the ADI orders every tract in the country, so the tenths and fifths below are national groups of tracts. Figure 7 compares the least deprived and the most deprived tenth of the 81,038 tracts with a rank. The ACS measures cover people aged 16 and over. Unemployment among residents was 4.9% (95% CI 4.3% to 5.4%) in the least deprived tenth and 8.6% (95% CI 8.2% to 9.0%) in the most deprived. Labor force participation was 66.1% (95% CI 65.4% to 66.7%) and 54.8% (95% CI 54.3% to 55.3%), and the share of workers who work from home was 23.4% (95% CI 22.0% to 25.3%) and 5.9% (95% CI 5.7% to 6.2%). The share of commuting workers, those who do not work from home, with a commute of an hour or more was 12.0% (95% CI 10.1% to 14.3%) in the least deprived tenth and 7.3% (95% CI 6.9% to 7.7%) in the most deprived, so long commutes are more common where deprivation is lower. Among jobs held by residents, 60.1% (95% CI 57.1% to 63.3%) were in the top band in the least deprived tenth and 30.4% (95% CI 29.9% to 30.9%) in the most deprived. The index includes tract unemployment, income, the white-collar share of employed people and the share of households without a vehicle, so every gradient here overlaps with it and is partly mechanical.

Figure 7

In the most deprived tenth of tracts, 30.4% of jobs held by residents were in the top earnings band against 60.1% in the least deprived tenth, and unemployment was 8.6% against 4.9%

Five measures of the residents of tracts in the least and the most deprived tenth by Area Deprivation Index rank, percent; the index is built partly from income, employment and vehicle access, so all five gradients are partly mechanical

Least deprived tenth of tracts
Most deprived tenth of tracts

Show data

Data for In the most deprived tenth of tracts, 30.4% of jobs held by residents were in the top earnings band against 60.1% in the least deprived tenth, and unemployment was 8.6% against 4.9%
least	least_hi	least_lo	measure	most	most_hi	most_lo	tracts_least	tracts_most
4.92	5.41	4.35	Unemployment rate (ACS 2019-2023)	8.65	9.05	8.25	8021	8103
66.12	66.72	65.45	Labor force participation (ACS 2019-2023)	54.82	55.27	54.35	8021	8103
12.02	14.28	10.11	Commuting workers with a commute of an hour or more (ACS 2019-2023)	7.29	7.73	6.88	8021	8103
23.44	25.27	21.97	Workers who work from home (ACS 2019-2023)	5.94	6.19	5.69	8021	8103
60.07	63.33	57.06	Jobs held by residents in the top earnings band (LODES 2019)	30.41	30.94	29.93	8014	8103

Unemployment is higher in the most deprived tenth and the other four measures are lower, including the share of commuting workers with a commute of an hour or more. Each dot is a mean over the tracts of one tenth, weighted by tract population for the ACS rows and by jobs held by residents for the LODES row, with a 95% interval from a bootstrap over counties. The index includes tract unemployment, income, the white-collar share of employed people and the share of households without a vehicle, so every gradient here overlaps with it and is partly mechanical.Note: The Area Deprivation Index (rank 1 least deprived, 100 most deprived) averages block-group ranks to tracts without population weights and understates deprivation in high-cost cities. Tenths hold near-equal numbers of the 81,038 tracts with a rank (tied ranks stay together); 1,580 tracts have none.Note: The ACS rows describe residents: the unemployment rate is a share of the civilian labor force aged 16 and over, participation a share of people aged 16 and over, the commute row a share of workers who commute (workers who do not work from home) and the work-from-home row a share of all workers. The staged ACS file has no margins of error, so the intervals show variation across counties and not the precision of single tracts.Note: The last row counts jobs held by residents, which LODES places at the worker's home tract. Alaska, Massachusetts and Puerto Rico are left out of the whole figure so that one set of tracts is compared; Connecticut tracts are matched through the tract crosswalk.Source: American Community Survey 2019-2023 tracts, population-weighted, no margins of error; LEHD LODES residence table 2019, jobs-weighted; Area Deprivation Index 2023, national rank

The jobs located in a tract can differ from the jobs its residents hold, and the difference depends on the county (Figure 8). In metropolitan counties the top-band share of jobs held by residents fell from 57.5% (95% CI 54.5% to 60.5%) in the least deprived fifth of tracts to 32.6% (95% CI 32.1% to 33.2%) in the most deprived. The share of jobs located in the same tracts fell from 53.3% (95% CI 50.0% to 56.5%) to 43.7% (95% CI 42.6% to 44.6%). In the most deprived fifth the gap between the top-band share of jobs located and that of jobs held by residents was +11.0 (95% CI 10.1 to 11.9) points in metropolitan counties, +1.9 (95% CI 1.4 to 2.4) points in nonmetropolitan counties with larger towns and −0.2 (95% CI −0.8 to 0.3) points in those with smaller towns or none, an interval that includes zero. Because the index includes tract income and employment, the fall in the share of jobs held by residents across the fifths is partly mechanical. The staged data have no tract-to-tract flows, so they cannot say whether the better-paid jobs in deprived metropolitan tracts are held by the tracts' residents or by people who commute in.

Figure 8

In the most deprived fifth of tracts in metropolitan counties, 43.7% of jobs located there were in the top earnings band against 32.6% of jobs held by the tracts' residents

Percent of jobs paying more than $3,333 a month, jobs held by residents of a tract against jobs located in it, by fifth of tracts by Area Deprivation Index rank within each county type, 2019

Jobs paying more than the top-band cut (percent of jobs)

Jobs held by residents of the tract
Jobs located in the tract

Fifth of tracts by Area Deprivation Index rank (1 least deprived, 5 most deprived)

Show data

Data for In the most deprived fifth of tracts in metropolitan counties, 43.7% of jobs located there were in the top earnings band against 32.6% of jobs held by the tracts' residents
conf_high	conf_low	estimate	fifth	group	kind	n_tracts
60.46	54.48	57.50	1	Metropolitan counties	Jobs held by residents of the tract	15,826
56.54	49.97	53.25	1	Metropolitan counties	Jobs located in the tract	15,826
53.33	51.12	52.25	2	Metropolitan counties	Jobs held by residents of the tract	15,350
50.22	47.63	48.88	2	Metropolitan counties	Jobs located in the tract	15,350
47.57	45.97	46.76	3	Metropolitan counties	Jobs held by residents of the tract	14,289
45.88	44.03	44.94	3	Metropolitan counties	Jobs located in the tract	14,289
41.50	40.41	40.94	4	Metropolitan counties	Jobs held by residents of the tract	12,062
44.35	42.48	43.44	4	Metropolitan counties	Jobs located in the tract	12,062
33.23	32.06	32.61	5	Metropolitan counties	Jobs held by residents of the tract	9,927
44.59	42.64	43.65	5	Metropolitan counties	Jobs located in the tract	9,927
44.87	41.62	43.26	1	Nonmetropolitan counties with larger towns	Jobs held by residents of the tract	281
41.93	36.76	39.27	1	Nonmetropolitan counties with larger towns	Jobs located in the tract	281
46.84	43.78	45.27	2	Nonmetropolitan counties with larger towns	Jobs held by residents of the tract	701
43.21	37.96	40.49	2	Nonmetropolitan counties with larger towns	Jobs located in the tract	701
45.03	43.60	44.31	3	Nonmetropolitan counties with larger towns	Jobs held by residents of the tract	1,626
42.12	39.17	40.59	3	Nonmetropolitan counties with larger towns	Jobs located in the tract	1,626
42.01	41.22	41.62	4	Nonmetropolitan counties with larger towns	Jobs held by residents of the tract	3,183
40.59	39.09	39.84	4	Nonmetropolitan counties with larger towns	Jobs located in the tract	3,183
35.71	34.90	35.31	5	Nonmetropolitan counties with larger towns	Jobs held by residents of the tract	4,161
37.81	36.61	37.21	5	Nonmetropolitan counties with larger towns	Jobs located in the tract	4,161
43.36	38.56	40.92	2	Nonmetropolitan counties with smaller towns or none	Jobs held by residents of the tract	84
39.39	31.68	35.51	2	Nonmetropolitan counties with smaller towns or none	Jobs located in the tract	84
42	40.01	41.03	3	Nonmetropolitan counties with smaller towns or none	Jobs held by residents of the tract	376
40.68	36.57	38.61	3	Nonmetropolitan counties with smaller towns or none	Jobs located in the tract	376
40.37	39.34	39.85	4	Nonmetropolitan counties with smaller towns or none	Jobs held by residents of the tract	1,068
40.06	37.85	38.96	4	Nonmetropolitan counties with smaller towns or none	Jobs located in the tract	1,068
35.85	35.08	35.47	5	Nonmetropolitan counties with smaller towns or none	Jobs held by residents of the tract	2,077
35.92	34.51	35.23	5	Nonmetropolitan counties with smaller towns or none	Jobs located in the tract	2,077

In metropolitan counties the line for residents' jobs falls from the least to the most deprived fifth and crosses below the line for jobs located. In the most deprived fifth the gap, jobs located minus jobs held by residents, is +11.0 points in metropolitan counties, +1.9 in the nonmetropolitan counties with larger towns and −0.2 in those with smaller towns or none. Each point is a pooled share with a 95% interval from a bootstrap over counties, and because the index is built partly from income and employment, the fall in the residents' line is partly mechanical.Note: Fifths hold near-equal numbers of tracts nationally (tied ranks stay together), so a county type has few tracts in the fifths it rarely contains. A cell with fewer than 30 tracts is not drawn, which leaves out the least deprived fifth of nonmetropolitan counties with smaller towns or none.Note: Jobs located are placed by LODES at the workplace tract and jobs held by residents at the home tract, so the two lines describe different groups of people in the same tracts. 3.1% of jobs located and 0.3% of jobs held by residents are in tracts with no index rank and are not in the figure.Note: Earnings bands are fixed in current dollars, so shares are compared within 2019 only. County types follow the county of the tract.Source: LEHD LODES residence and workplace tables, 2019 (Alaska and Massachusetts left out), jobs-weighted; Area Deprivation Index 2023, national rank; county types from the USDA 2023 Rural-Urban Continuum Codes

Whether residents of poor neighborhoods can reach the jobs that match their skills has a long history in economics under the name of spatial mismatch, which began with a study of housing segregation and the suburbanization of employment.[7] This chapter's tract figures show where jobs and workers are by tract. They do not measure travel time, transit or the matching of skills to openings, and they cannot test that hypothesis. The earnings that college graduates reach are in Chapter 3.2, College access, price and earnings.

What these data cannot show

All the patterns here are descriptions of counties and tracts. The pooled rate of a county type, the share of jobs in the top band for a group of counties and the gradient across groups of tracts say nothing certain about any worker.

Unemployment and pay come from different groups. LAUS and the ACS count residents, QCEW counts jobs located in a county, and LODES counts jobs at either end of the commute, so no figure here gives the pay of a county's residents in dollars. LODES shows only three bands, and a fixed band in current dollars holds more jobs each year as prices and pay rise, so the shares apply to 2019 and say nothing about change over time. The same dollar cut also buys different amounts in different places, so top-band shares are not comparisons of purchasing power. Counting jobs and not people means a worker with two jobs is counted once for each, and noise added for confidentiality makes the shares of small counties and tracts imprecise.

The county unemployment estimates are modeled and the county file carries no margin of error, the intervals here reflect which places are in each group, and a high rate in a county with a small labor force can rest on few people. Connecticut is drawn as its legacy counties, and the unemployment values there are spread from planning regions by population, so they are approximate. Puerto Rico's 78 municipios are in the LAUS file and are not drawn. The ACS tract file has no margins of error, and a cell of county type by fifth of tracts is drawn only with at least 30 tracts, which leaves out the least deprived fifth of the nonmetropolitan counties with smaller towns or none. The ADI averages block-group ranks without population weights, leaves 1,580 tracts without a rank, and understates deprivation in high-cost cities. Jobs in tracts with no rank are left out of the tract figures: 3.1% of jobs located and 0.3% of jobs held by residents. A tract has no rank when every block group in it is suppressed, for example for group quarters or small populations.

LODES is incomplete for some state-years, and the residence table is almost empty where a state has no workplace table or only a stub. Alaska has no workplace file for 2019, and its residence table holds 3,090 jobs for that year against 308,964 in 2015. The Massachusetts workplace file for 2010 is a stub of 2 tracts and 17 jobs, and its residence table holds 151,718 jobs against 3,330,566 in 2015. A stub reads as a complete file unless the jobs are compared, so the build compares workplace jobs with residence jobs. Alaska and Massachusetts are left out of every LODES figure, so every year of the commuting trend covers the same states. The 2022 files lack Alaska and Michigan, so 2019 is the year used for residents against workplaces. Puerto Rico is not drawn and is not in the LODES tables used here.

The comparison of residents with workplaces is for counties and tracts. It cannot say who holds the better-paid jobs in a deprived neighborhood, whether commuting costs or hiring practices keep residents from them, or how many workers would take them if they could.

Data sources

BLS Local Area Unemployment StatisticsCounty annual-average unemployment, 2000 to 2024 (map: 2023); residents by county of residenceYears: 1990-2024 · Weights: labor force of each county for pooled rates; each county once for distributions · License: Public domain (U.S. federal)
BLS Quarterly Census of Employment and WagesCounty total-covered employment and wages, 2019 and 2023; jobs by county of the workplaceYears: 1990-2024 · Weights: none (full counts of covered payroll); jobs in each county for pooled pay · License: Public domain (U.S. federal)
LEHD Origin-Destination Employment StatisticsResidence, workplace and origin-destination tables, 2010, 2015 and 2019 (Alaska and Massachusetts left out)Years: 2002-2022 · Weights: jobs in each table; bootstrap over states for counties and over counties for tracts · License: Public domain (U.S. federal)
American Community Survey2019-2023 tract unemployment, labor force participation, commute and work-from-home sharesYears: 2019-2023 (5-year) · Weights: 2019-2023 tract population · License: Public domain (U.S. Census Bureau)
Area Deprivation IndexArea Deprivation Index 2023, national rank by tractYears: 2023 release (version 4.0.1; built on ACS 2019-2023) · Weights: none (index); tract values are unweighted means of block-group ranks · License: Free for non-commercial use; cite the Neighborhood Atlas and Kind and Buckingham
Rural-Urban Continuum Codes2023 Rural-Urban Continuum Codes for county types and 2020 county population for correlation weightsYears: 2023 · Weights: 2020 county population · License: Public domain (U.S. federal)

All data sources

Methods

This chapter compares where residents live with where jobs are, using residents (LAUS, the ACS and the LODES residence table), workplaces (QCEW and the LODES workplace table) and the LODES flows between home county and work county. LODES counts jobs, not people, and its earnings come in three fixed bands in current dollars, so shares are compared within one year. Alaska and Massachusetts are left out of every LODES figure because a workplace file is absent or a stub, and counties enter the scatter and correlations only with enough jobs in each table.

Residents, workplaces and flows

Three populations are kept apart. Residents are counted where they live (LAUS, the ACS, and the LODES residence table, which places a job at the worker's home). Workplaces are counted where the job is (QCEW and the LODES workplace table). Flows are the LODES origin-destination pairs of home county and work county.

LODES counts jobs, not people: its job is a worker-employer match with positive earnings in the second quarter and the quarter before, and a worker with two jobs is counted twice.

LODES earnings come in three fixed bands of monthly earnings in current dollars (up to $1,250, $1,251 to $3,333, above $3,333); 'top band' means above $3,333. Because the bands do not move with prices, shares are compared within one year.

Figure explained here: Figure 4

Which states and years LODES covers

LODES scope: a state-year is incomplete when its workplace table is absent or holds under half of its residence-table jobs (found from the jobs, since a stub file counts as a file).

Alaska (no 2019 workplace file) and Massachusetts (a stub workplace file for 2010, with 17 jobs) are left out of every LODES figure, so 2010, 2015 and 2019 cover the same states; 2019 is used for residents against workplaces, and 2022 is not used because Alaska and Michigan lack workplace files.

County types and how rates and pay are measured

County types follow the 2023 USDA Rural-Urban Continuum Code of the county: 1 to 3 metropolitan, 4 to 7 nonmetropolitan with an urban population of 5,000 or more, 8 and 9 nonmetropolitan with an urban population under 5,000. Connecticut's legacy counties (used by QCEW to 2023 and by LODES) take the type of the planning region that holds most of their population; the retired Alaska codes of LAUS take the type of their successors.

LAUS pooled rates are unemployed residents over labor force summed over counties, with Puerto Rico left out; county distributions count each county once. The 7 Louisiana parishes without an annual average in 2005 and 2006 leave those two years.

QCEW pay is total wages over average employment; a county whose totals the Bureau withholds is missing, never zero, and is left out.

Figures explained here: Figure 2, Figure 1 and Figure 3

Weights, fifths and tenths

County correlations are weighted by 2020 county population (ERS file). Tract rows weight ACS measures by tract population and LODES shares by jobs.

County fifths hold near-equal numbers of counties (they differ by at most one), tract fifths and tenths near-equal numbers of tracts (tied ADI ranks stay together). Cuts: counties enter the scatter and correlations with at least 1,000 jobs in each table; a tract cell is drawn with at least 30 tracts.

Figures explained here: Figure 5, Figure 6, Figure 7 and Figure 8

How uncertainty is measured

Intervals are 95% percentile intervals from 2,000 draws of whole units with replacement (seed 12345): states for county figures, because counties in a state share policies and labor markets, and counties for tract figures.

A difference between two pooled estimates uses one set of draws.

References

McKenzie, Brian; Burd, Charlynn (2015). Where Do All Those Commuters Come From? Census Bureau Releases New Information on County-to-County Commuting Flows. U.S. Census Bureau, Random Samplings blog (August 13, 2015). www.census.gov/newsroom/blogs/random-samplings/2015/08/where-do-all-those-commuters-come-from-census-bureau-releases-new-information-on-county-to-county-commuting-flows.html
U.S. Bureau of Labor Statistics (2018). Local Area Unemployment Statistics: Calculation. BLS Handbook of Methods (page last modified January 9, 2018). www.bls.gov/opub/hom/lau/calculation.htm
U.S. Bureau of Labor Statistics (2026). Quarterly Census of Employment and Wages: Concepts. BLS Handbook of Methods (page last modified August 31, 2026). www.bls.gov/opub/hom/cew/concepts.htm
U.S. Census Bureau, Longitudinal Employer-Household Dynamics Program (2025). LEHD Origin-Destination Employment Statistics (LODES) Dataset Structure, Format Version 8.4. U.S. Census Bureau technical documentation (revised December 3, 2025). lehd.ces.census.gov/data/lodes/LODES8/LODESTechDoc8.4.pdf
U.S. Census Bureau, Longitudinal Employer-Household Dynamics Program (2025). OnTheMap: Data Overview (LODES Version 8). U.S. Census Bureau technical documentation (revised December 2, 2025). lehd.ces.census.gov/doc/help/onthemap/OnTheMapDataOverview.pdf
Kind, Amy J. H.; Buckingham, William R. (2018). Making Neighborhood-Disadvantage Metrics Accessible: The Neighborhood Atlas. New England Journal of Medicine 378(26): 2456-2458. doi:10.1056/NEJMp1802313
Kain, John F. (1968). Housing Segregation, Negro Employment, and Metropolitan Decentralization. The Quarterly Journal of Economics 82(2): 175-197. doi:10.2307/1885893

How to cite

@misc{singh2026workwagesandaccesstojobs,
  author = {Singh, Vishal},
  title = {Work, wages and access to jobs},
  year = {2026},
  url = {https://vishalsingh.org/zipcode/work-wages-and-access-to-jobs},
  note = {In ZIP Code Destiny. Published 9 October 2026.}
}