Chapter 1.2 · Two zooms

What a county average leaves out

A county's percentile on a deprivation index follows the average of its tracts' percentiles, with a correlation of 0.96, yet 32.7% of residents live in a tract more than 25 percentile points from their county's percentile.

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

Built from its own census estimates, Los Angeles County ranks at the 29.1 percentile of a deprivation index on which a higher percentile means more deprived. Yet 53.0% of its residents live in a census tract whose percentile is more than 25 points away from the county's. Los Angeles County is the most populous county, and its single number stands for neighborhoods on both sides of it. This chapter builds one index for tracts and for counties and asks what a county's percentile says about the tracts inside it.

A county's rank follows the average of its tracts

A county can be given a percentile in two ways. The first recomputes the index from the county's own estimates, such as the median income of all its families, and ranks the county on that score. The second averages the percentiles of the county's tracts, weighting each tract by its residents. Across counties weighted by residents, the two correlate 0.96. For a question about counties as units, the county file and the tract file give nearly the same order.

The view from a resident differs. Give each resident two numbers, the percentile of their tract and the percentile of their county, and take the difference (Figure 1). Among all residents of the tracts studied, in the American Community Survey for 2019 to 2023, 32.7% live in a tract more than 25 points from their county's percentile. The difference is close to even in sign, with 46.8% of residents living in a tract that ranks as more deprived than their county.

Figure 1

Counted among residents, 32.7% of people live in a tract more than 25 percentile points from their county's percentile, and 40.7% when tracts and counties are ranked as units

Population-weighted distribution of a resident's tract percentile minus the resident's county percentile, in bins of five percentile points, 309.9 million residents of 76,496 tracts, ACS 2019 to 2023

Show data

Data for Counted among residents, 32.7% of people live in a tract more than 25 percentile points from their county's percentile, and 40.7% when tracts and counties are ranked as units
conf_high	conf_low	convention	n	share	x0	x1
0.024	0	Among residents	5	0.009	−95	−90
0.025	0.003	Among residents	12	0.013	−90	−85
0.095	0.019	Among residents	48	0.053	−85	−80
0.136	0.045	Among residents	72	0.086	−80	−75
0.220	0.096	Among residents	119	0.153	−75	−70
0.424	0.196	Among residents	241	0.300	−70	−65
0.651	0.324	Among residents	345	0.465	−65	−60
0.841	0.534	Among residents	535	0.680	−60	−55
1.24	0.808	Among residents	750	1.02	−55	−50
1.70	1.16	Among residents	1,050	1.42	−50	−45
2.12	1.59	Among residents	1,366	1.85	−45	−40
2.77	2.19	Among residents	1,801	2.48	−40	−35
3.62	2.97	Among residents	2,451	3.29	−35	−30
4.81	3.93	Among residents	3,246	4.34	−30	−25
5.52	4.75	Among residents	3,941	5.13	−25	−20
6.41	5.69	Among residents	4,717	6.04	−20	−15
7.58	6.71	Among residents	5,618	7.11	−15	−10
9.10	8.06	Among residents	6,675	8.57	−10	−5
10.88	9.51	Among residents	7,779	10.17	−5	0
9.69	8.27	Among residents	6,783	8.94	0	5
7.31	6.53	Among residents	5,316	6.91	5	10
6.16	5.57	Among residents	4,464	5.86	10	15
5.00	4.48	Among residents	3,656	4.74	15	20
4.12	3.60	Among residents	3,006	3.87	20	25
3.82	3.16	Among residents	2,664	3.49	25	30
3.01	2.48	Among residents	2,102	2.75	30	35
2.50	1.96	Among residents	1,678	2.24	35	40
2.15	1.54	Among residents	1,475	1.85	40	45
1.90	1.32	Among residents	1,214	1.61	45	50
1.60	1.01	Among residents	981	1.30	50	55
1.37	0.768	Among residents	765	1.04	55	60
1.16	0.542	Among residents	575	0.800	60	65
1.03	0.453	Among residents	521	0.697	65	70
0.380	0.208	Among residents	217	0.287	70	75
0.352	0.148	Among residents	164	0.240	75	80
0.192	0.067	Among residents	83	0.122	80	85
0.113	0.033	Among residents	47	0.068	85	90
0.028	0.005	Among residents	11	0.015	90	95
0.004	0	Among residents	2	0.001	95	100
0.002	0	Among tracts and counties	1	0.001	−85	−80
0.009	0	Among tracts and counties	2	0.003	−80	−75
0.042	0.002	Among tracts and counties	11	0.017	−75	−70
0.035	0	Among tracts and counties	11	0.013	−70	−65
0.078	0.019	Among tracts and counties	43	0.045	−65	−60
0.118	0.033	Among tracts and counties	62	0.072	−60	−55
0.175	0.066	Among tracts and counties	104	0.115	−55	−50
0.245	0.103	Among tracts and counties	138	0.166	−50	−45
0.337	0.184	Among tracts and counties	210	0.255	−45	−40
0.501	0.304	Among tracts and counties	310	0.392	−40	−35
0.687	0.410	Among tracts and counties	424	0.538	−35	−30
1.06	0.666	Among tracts and counties	667	0.845	−30	−25
1.37	0.961	Among tracts and counties	921	1.16	−25	−20
1.99	1.46	Among tracts and counties	1,352	1.72	−20	−15
2.81	2.18	Among tracts and counties	1,940	2.49	−15	−10
4.20	3.52	Among tracts and counties	3,023	3.85	−10	−5
7.19	6.48	Among tracts and counties	5,226	6.83	−5	0
11.42	9.67	Among tracts and counties	7,717	10.46	0	5
9.77	8.76	Among tracts and counties	6,789	9.26	5	10
9.05	8.27	Among tracts and counties	6,399	8.65	10	15
8.16	7.40	Among tracts and counties	5,885	7.78	15	20
7.48	6.71	Among tracts and counties	5,425	7.09	20	25
6.37	5.85	Among tracts and counties	4,652	6.10	25	30
5.63	5.14	Among tracts and counties	4,208	5.39	30	35
4.84	4.38	Among tracts and counties	3,597	4.60	35	40
4.13	3.67	Among tracts and counties	3,044	3.89	40	45
3.70	3.26	Among tracts and counties	2,730	3.48	45	50
3.25	2.77	Among tracts and counties	2,405	3.02	50	55
2.98	2.42	Among tracts and counties	2,134	2.71	55	60
2.71	2.04	Among tracts and counties	1,859	2.38	60	65
2.22	1.68	Among tracts and counties	1,530	1.97	65	70
1.95	1.36	Among tracts and counties	1,302	1.66	70	75
1.70	1.10	Among tracts and counties	1,095	1.40	75	80
1.32	0.702	Among tracts and counties	753	0.987	80	85
0.764	0.303	Among tracts and counties	402	0.497	85	90
0.215	0.100	Among tracts and counties	116	0.156	90	95
0.023	0.003	Among tracts and counties	8	0.011	95	100

Both distributions peak near zero, the one that ranks by residents is centered on zero and the other is centered to the right. Each bar is the share of residents whose tract percentile minus their county's percentile falls in the bin, so positive values mean the tract is more deprived than the county. The second distribution ranks each tract and each county once. Values in Table 2.

Note: Percentiles come from the index in each box. Among residents, a unit's percentile is the share of residents of its box who live in units that score lower; among tracts and counties, it is the share of units that score lower.Note: Intervals in the tooltip are 95% intervals from a bootstrap over counties (replicates and seed in the methods note). One tract has no county in the county frame and is left out.Source: ACS 2019-2023 five-year estimates (tracts and counties); population-weighted (bins: shares of residents)

How we know

The index is the first principal component of census measures from the American Community Survey (ACS) for 2019 to 2023, among them family income, home value, rent, the poverty rate, schooling and homeownership. One recipe builds it in two boxes. A box is the set of units compared, here 76,496 census tracts in one and the counties that contain them in the other. In each box a higher score means more deprived.Counties here include Connecticut's planning regions and Puerto Rico's municipios. The share of single-parent households is left out of both boxes because the tract and county files define it differently. In the county box every measure is the county's own ACS estimate and not an average of its tracts. In this chapter a percentile is the share of the residents of a box who live in units that score lower, so it counts people rather than places. The index is not the published Area Deprivation Index (ADI), which sums its measures in their own units and so follows home value.[1, 2] The ACS files carry no margins of error, so the only intervals come from resampling whole counties. Part of the spread of tracts within a county is sampling error, and the data cannot say how much. The report gives every estimate with its interval and sample size.

The lean toward more deprived tracts comes from the ranking

The usual practice ranks units, counting each tract and each county once, as the Neighborhood Atlas does for block groups.[3] Counties differ so much in size that this counts places and not people. Under that convention, 81.5% of residents live in a tract that ranks as more deprived than their county, and the second distribution in Figure 1 is centered to the right of zero.

That lean follows from the reference population. Ranked among counties counted once, the average resident lives in a county at the 26.5 percentile, below the middle of the county ranking. The average resident's tract sits near the middle of the tract ranking. The choice of convention moves a statement about residents, as the two distributions in Figure 1 show.

Counties and their tracts agree in order but not level

Averaging the tracts' percentiles and recomputing the index from county estimates give nearly the same order of counties, and they give different levels. A county's own percentile lies farther from the middle of the scale than the mean of its tracts' percentiles in counties that hold 96.3% of residents. A county at an end of the county ranking has, on average, neighborhoods that are less extreme than the county itself.

Figure 2 draws the 30 most populous counties this way. In each of them the county percentile lies inside the spread of its residents' tracts. The counties are chosen by population, since the spread of a small county rests on few tracts.

Figure 2

Los Angeles County has a county percentile of 29.1, and its residents' tracts run from 2.0 to 94.2

Percentile among residents of the county index built from the county's own estimates, beside the average and the 5th to 95th percentile range of the percentiles of its tracts, the 30 most populous counties

County's own percentile
Average of its tracts' percentiles

Show data

Data for Los Angeles County has a county percentile of 29.1, and its residents' tracts run from 2.0 to 94.2
conf_high	conf_low	county	measure	n	percentile
n/a	n/a	Santa Clara County, CA	County's own percentile	n/a	0.500
59.50	0.100	Santa Clara County, CA	Average of its tracts' percentiles	392	13.50
n/a	n/a	Nassau County, NY	County's own percentile	n/a	2.10
63.50	1.10	Nassau County, NY	Average of its tracts' percentiles	217	16.10
n/a	n/a	Alameda County, CA	County's own percentile	n/a	2.60
80.40	0.300	Alameda County, CA	Average of its tracts' percentiles	357	23.40
n/a	n/a	Middlesex County, MA	County's own percentile	n/a	3.50
68.70	0.900	Middlesex County, MA	Average of its tracts' percentiles	328	20
n/a	n/a	King County, WA	County's own percentile	n/a	4.40
73	0.800	King County, WA	Average of its tracts' percentiles	476	21.40
n/a	n/a	Suffolk County, NY	County's own percentile	n/a	6.40
53.80	2.80	Suffolk County, NY	Average of its tracts' percentiles	311	19.60
n/a	n/a	Orange County, CA	County's own percentile	n/a	8.30
81.90	1.40	Orange County, CA	Average of its tracts' percentiles	587	30.30
n/a	n/a	New York County, NY	County's own percentile	n/a	9.40
93.90	1.70	New York County, NY	Average of its tracts' percentiles	217	31.60
n/a	n/a	San Diego County, CA	County's own percentile	n/a	13.20
86.50	1.10	San Diego County, CA	Average of its tracts' percentiles	684	31.80
n/a	n/a	Los Angeles County, CA	County's own percentile	n/a	29.10
94.20	2	Los Angeles County, CA	Average of its tracts' percentiles	2,226	46.20
n/a	n/a	Queens County, NY	County's own percentile	n/a	32.10
91.70	14.30	Queens County, NY	Average of its tracts' percentiles	612	49.70
n/a	n/a	Sacramento County, CA	County's own percentile	n/a	35.20
92	6	Sacramento County, CA	Average of its tracts' percentiles	341	46.40
n/a	n/a	Palm Beach County, FL	County's own percentile	n/a	36.30
90.90	4.40	Palm Beach County, FL	Average of its tracts' percentiles	321	44.50
n/a	n/a	Riverside County, CA	County's own percentile	n/a	37.20
89.90	6.70	Riverside County, CA	Average of its tracts' percentiles	491	45.20
n/a	n/a	Maricopa County, AZ	County's own percentile	n/a	38.40
93.10	6.60	Maricopa County, AZ	Average of its tracts' percentiles	911	44.70
n/a	n/a	Kings County, NY	County's own percentile	n/a	40.20
93.60	3.40	Kings County, NY	Average of its tracts' percentiles	651	53.30
n/a	n/a	Tarrant County, TX	County's own percentile	n/a	42.60
94.60	5.70	Tarrant County, TX	Average of its tracts' percentiles	406	46.60
n/a	n/a	Broward County, FL	County's own percentile	n/a	44.70
90.40	5	Broward County, FL	Average of its tracts' percentiles	381	47.40
n/a	n/a	Orange County, FL	County's own percentile	n/a	48.80
91.30	10.10	Orange County, FL	Average of its tracts' percentiles	244	49.60
n/a	n/a	Hillsborough County, FL	County's own percentile	n/a	50.60
91.10	8.10	Hillsborough County, FL	Average of its tracts' percentiles	297	47.70
n/a	n/a	San Bernardino County, CA	County's own percentile	n/a	52
94.50	8.10	San Bernardino County, CA	Average of its tracts' percentiles	432	55.10
n/a	n/a	Cook County, IL	County's own percentile	n/a	54
96	5	Cook County, IL	Average of its tracts' percentiles	1,221	51.50
n/a	n/a	Miami-Dade County, FL	County's own percentile	n/a	57.50
94.10	6.70	Miami-Dade County, FL	Average of its tracts' percentiles	606	53.10
n/a	n/a	Dallas County, TX	County's own percentile	n/a	60.40
95.70	4	Dallas County, TX	Average of its tracts' percentiles	548	57.10
n/a	n/a	Clark County, NV	County's own percentile	n/a	63.80
95.70	14.90	Clark County, NV	Average of its tracts' percentiles	502	55.10
n/a	n/a	Bexar County, TX	County's own percentile	n/a	67.20
97.40	11.60	Bexar County, TX	Average of its tracts' percentiles	352	57.80
n/a	n/a	Harris County, TX	County's own percentile	n/a	69.50
97.60	5.90	Harris County, TX	Average of its tracts' percentiles	909	56.10
n/a	n/a	Philadelphia County, PA	County's own percentile	n/a	89.60
99.10	14.90	Philadelphia County, PA	Average of its tracts' percentiles	342	69.30
n/a	n/a	Bronx County, NY	County's own percentile	n/a	92.90
98.50	20.10	Bronx County, NY	Average of its tracts' percentiles	188	72.40
n/a	n/a	Wayne County, MI	County's own percentile	n/a	93.60
99.50	13.80	Wayne County, MI	Average of its tracts' percentiles	488	67.50

In every county the county dot lies inside the line for its tracts, and in 28 of them it sits farther from the middle than the average of those tracts. The first dot is the percentile of the index built from the county's own estimates, and the second is the population-weighted mean of its tracts' percentiles. The line is the spread of its residents' tract percentiles given in the subtitle, not an interval for the county. Counties are the most populous in the county frame, ordered by the county percentile. Values in Table 3.

Note: The line is a spread of the county's tract percentiles, weighted by residents, and not a confidence interval. The county percentile has no interval because the staged county files carry no margins of error for the ACS estimates it rests on.Note: Tract percentiles are among residents of the tract frame. Tracts missing a home value, rent or mortgage median are outside the frame, so for counties where the frame holds less than 90% of residents (Nassau County, NY; Suffolk County, NY; New York County, NY; Kings County, NY; Miami-Dade County, FL; Dallas County, TX; Harris County, TX; Bronx County, NY; Wayne County, MI) the average and the spread describe only the tracts that remain.Note: Counties are chosen by population, and ordered by county percentile, because the spread of a small county rests on few tracts.Source: ACS 2019-2023 five-year estimates (tracts and counties); population-weighted (the line is a spread of tract percentiles, not an interval)

Across the country, 64.6% of the variance of the tract percentile lies between tracts of the same county, which a county percentile cannot see. That share depends on the index, and Chapter 10.1, Gradients, slopes and variance gets different shares when it splits the published ADI and a second index the same way.

Correlations change when the unit changes

The unit also moves correlations. Figure 3 correlates the index percentile with tract measures of life expectancy, upward mobility, poverty and homeownership, first across tracts and then across counties. A county's value of a measure is the resident-weighted mean of its tracts' values, so only the unit changes.

Life expectancy at birth, from USALEEP for deaths in 2010 to 2015, correlates −0.62 with the index across tracts and −0.76 across counties weighted by residents, a stronger relationship at the larger unit. Counting each county once gives −0.56, weaker than across tracts, so the strengthening depends on how counties are weighted. Under weights, the correlations with upward mobility and with poverty hold, and the one with homeownership disappears. Poverty and homeownership are measures inside the index, so their correlations are partly built in at any unit. The weight on homeownership in the county box has an interval that includes zero.

Figure 3

The index correlates −0.62 with life expectancy across tracts and −0.76 across counties, and −0.43 with homeownership across tracts and +0.06 across counties

Correlation of the index percentile among residents with four tract measures, across tracts and across counties, ACS 2019 to 2023, USALEEP 2010 to 2015 and Opportunity Atlas 1978 to 1983 cohorts

Tracts
Counties, weighted by residents
Counties, each counted once

Show data

Data for The index correlates −0.62 with life expectancy across tracts and −0.76 across counties, and −0.43 with homeownership across tracts and +0.06 across counties
conf_high	conf_low	n	outcome	r	zoom
−0.600	−0.640	71,874	Life expectancy at birth (deaths 2010 to 2015)	−0.621	Tracts
−0.717	−0.792	3,101	Life expectancy at birth (deaths 2010 to 2015)	−0.758	Counties, weighted by residents
−0.537	−0.587	3,101	Life expectancy at birth (deaths 2010 to 2015)	−0.562	Counties, each counted once
−0.555	−0.598	75,731	Upward mobility (children born 1978 to 1983)	−0.577	Tracts
−0.542	−0.640	3,155	Upward mobility (children born 1978 to 1983)	−0.593	Counties, weighted by residents
−0.331	−0.383	3,155	Upward mobility (children born 1978 to 1983)	−0.356	Counties, each counted once
0.788	0.775	76,496	Poverty rate (ACS 2019 to 2023)	0.782	Tracts
0.782	0.724	3,203	Poverty rate (ACS 2019 to 2023)	0.752	Counties, weighted by residents
0.665	0.634	3,203	Poverty rate (ACS 2019 to 2023)	0.649	Counties, each counted once
−0.401	−0.463	76,496	Homeownership rate (ACS 2019 to 2023)	−0.432	Tracts
0.175	−0.059	3,203	Homeownership rate (ACS 2019 to 2023)	0.065	Counties, weighted by residents
−0.032	−0.111	3,203	Homeownership rate (ACS 2019 to 2023)	−0.071	Counties, each counted once

The tract dots and the weighted county dots part for life expectancy and homeownership, drawn in color, and sit together for upward mobility and poverty. A dot is the correlation of the index percentile with a tract measure, or with its population-weighted mean over a county's tracts, with a 95% interval from a bootstrap over counties. The third dot counts each county once. Values in Table 4.

Note: A county's value of a measure is the population-weighted mean of its tracts' values, so only the unit changes between the tract dots and the county dots. Poverty and homeownership are ingredients of the index, so their correlations are partly built in; life expectancy and upward mobility are not.Note: Life expectancy is a modeled estimate for deaths in 2010 to 2015 on 2020 tracts, interpolated from 2010 tracts, with institutional tracts dropped; upward mobility is the Opportunity Atlas rank of children from low-income families, interpolated to 2020 tracts.Source: ACS 2019-2023; NCHS USALEEP 2010-2015; Opportunity Atlas 1978-1983 birth cohorts; population-weighted unless the row says each county is counted once

Gehlke and Biehl reported in 1934 that a correlation computed from census tract data depended on how the tracts were grouped.[4] The pattern is the modifiable areal unit problem. The results here come from one set of boundaries and show no general direction. A correlation across counties describes counties. Robinson showed that a correlation across areas can differ from the correlation across the people inside them.[5, 6] For a program run by counties the county box is the right one, and a finding in it is a finding about counties.

What this cannot tell you

A tract percentile and a county percentile are properties of places, and neither says how deprived any resident is. The tract box needs a published ADI rank and every measure, and it holds 92.4% of all residents. The tracts outside it are poorer and are concentrated in some counties, among them Bronx County and New York County, where the average and spread describe only the tracts that remain. Life expectancy, for deaths in 2010 to 2015, and upward mobility, for children born in 1978 to 1983, are older than the ACS measures. The correlations show how a recent index lines up with older outcomes.

Go deeper

The report: every estimate with its interval and sample size.
Tables: every figure of the report as a table.

Same question at another grain:

Deprivation index
- Chapter 1.1, What the deprivation index measures Neighborhoods

Reading paths that include this chapter:

Data sources

American Community SurveyACS 2019-2023 five-year estimates: the staged tract file, a Census API pull of housing, occupation, income-distribution and schooling tables for tracts, and the same pull for counties, Connecticut planning regions and Puerto Rico municipiosYears: 2019-2023 (5-year) · Weights: ACS 2019-2023 population of the tract or county · License: Public domain (U.S. Census Bureau)
Area Deprivation IndexNeighborhood Atlas ADI, 2023 release (version 4.0.1): a published rank is required for a tract to enter the tract frame, and the ADI is the comparison for the tract indexYears: 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
USALEEP life expectancyNCHS small-area life expectancy at birth, deaths 2010 to 2015, 2020 tracts (interpolated), institutional tracts droppedYears: 2010-2015 · Weights: ACS 2019-2023 tract population · License: Public domain (NCHS, CDC)
Opportunity AtlasMean adult household income rank of children from low-income families, 1978 to 1983 birth cohorts, 2020 tracts (interpolated)Years: 1978-1983 birth cohorts (2018 release) · Weights: ACS 2019-2023 tract population · License: Public use (Opportunity Insights and the U.S. Census Bureau)

All data sources

References

Singh, Gopal K. (2003). Area Deprivation and Widening Inequalities in US Mortality, 1969-1998. American Journal of Public Health 93(7): 1137-1143. doi:10.2105/AJPH.93.7.1137
Petterson, Stephen (2023). Deciphering the Neighborhood Atlas Area Deprivation Index: the consequences of not standardizing. Health Affairs Scholar 1(5): qxad063. doi:10.1093/haschl/qxad063
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
Gehlke, C. E.; Biehl, Katherine (1934). Certain Effects of Grouping upon the Size of the Correlation Coefficient in Census Tract Material. Journal of the American Statistical Association 29(185A): 169-170. doi:10.1080/01621459.1934.10506247
Robinson, W. S. (1950). Ecological Correlations and the Behavior of Individuals. American Sociological Review 15(3): 351-357. doi:10.2307/2087176
Subramanian, S. V.; Jones, Kelvyn; Kaddour, Afamia; Krieger, Nancy (2009). Revisiting Robinson: the perils of individualistic and ecologic fallacy. International Journal of Epidemiology 38(2): 342-360. doi:10.1093/ije/dyn359

How to cite

@misc{singh2026deprivationattwozooms,
  author = {Singh, Vishal},
  title = {What a county average leaves out},
  year = {2026},
  url = {https://vishalsingh.org/zipcode/deprivation-at-two-zooms},
  note = {In ZIP Code Destiny. Published 9 October 2026.}
}