Chapter 10.1 · The factory floor · Report

Gradients, slopes and variance

For deaths in 2010 to 2015, tract life expectancy is 1.12 years lower per ten-point step of the ADI within counties, and lower in more deprived tracts in all 267 metros.

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

Every estimate of the chapter with its interval and every figure as a table, generated from the chapter's data files. Table n of this report is figure n of the chapter. The same report as Markdown: report.md.

Identification

Title

Gradients, slopes and variance

Label

The factory floor

Chapter

10.1 (Part X, How the estimates are made; Methods and sources)

Question

How are the book's gradients estimated, and how much of the variation lies between states, between counties and within counties?

Dek

For deaths in 2010 to 2015, tract life expectancy is 1.12 years lower per ten-point step of the ADI within counties, and lower in more deprived tracts in all 267 metros.

Grain

neighborhoods

Status

published

Published

9 October 2026

Updated

not revised

Data sources

USALEEP life expectancy. USALEEP life expectancy at birth, 2010-2015, on 2020 tracts (area-weighted from 2010 tracts), institutional tracts dropped; years: 2010-2015; weights: ACS 2019-2023 tract population
Area Deprivation Index. Neighborhood Atlas Area Deprivation Index 2023, national percentile, tracts; years: 2023 release (version 4.0.1; built on ACS 2019-2023); weights: ACS 2019-2023 tract population (each tract once for the index split by county)
American Community Survey. ACS 2019-2023 tracts: population, the nine measures of the index and the measures of the correlations; years: 2019-2023 (5-year); weights: tract population; each tract once in the index
Census TIGER/Line tracts. Census 2020 county file: the core-based statistical area code of each county (CBSAFP); years: 2020; weights: none (assignment of counties to metros)
CDC PLACES. CDC PLACES 2024 release, tracts: six measures of the coefficient zoo; years: 2024 release (tracts); 2025 release (ZCTAs, 2023 data year); weights: adult population of each tract (PLACES)
CDC WONDER natality. CDC WONDER natality 2018-2024, county by birthweight class: counts of withheld cells; years: 2018-2024; weights: none (full counts of cells)

Canonical URL

https://vishalsingh.org/zipcode/gradients-slopes-and-variance

Markdown

https://vishalsingh.org/zipcode/gradients-slopes-and-variance/report.md

BibTeX

@misc{singh2026gradientsslopesandvariance,
  author = {Singh, Vishal},
  title = {Gradients, slopes and variance},
  year = {2026},
  url = {https://vishalsingh.org/zipcode/gradients-slopes-and-variance},
  note = {In ZIP Code Destiny. Published 9 October 2026.}
}

Principal estimates

The facts the dek and the opening prose cite, in order of appearance. Every fact is in the table of all estimates below.

Estimand	Population	Period	Value	Interval	n	Weights	Source	Fact id
population-weighted least-squares slope of tract life expectancy on the ADI national percentile, times ten, sign reversed, with a constant for each county; cluster-robust interval (t on the number of counties minus one)	residents of non-institutional tracts in the 50 states and DC with a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population	1.12	95% CI 1.07 to 1.16	77,722 tracts	ACS 2019-2023 tract population	USALEEP life expectancy	ladder_county
count of metros	residents of metros (core-based statistical areas) with at least 40 tracts that have a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population	267			none (full count)	USALEEP life expectancy	n_metros
population-weighted least-squares slope of tract life expectancy on the ADI national percentile, times ten, sign reversed; cluster-robust interval (t on the number of states minus one)	residents of non-institutional tracts in the 50 states and DC with a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population	0.84	95% CI 0.75 to 0.92	77,936 tracts	ACS 2019-2023 tract population	USALEEP life expectancy	ladder_raw
count of tracts in the regressions	residents of non-institutional tracts in the 50 states and DC with a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population	77,936			none (full count)	USALEEP life expectancy	n_ladder_tracts

Findings

Life expectancy at birth is 0.84 (95% CI 0.75 to 0.92) years lower for each ten-point step up the Area Deprivation Index (ADI) national percentile across the 77,936 census tracts that have an ADI rank and a USALEEP estimate for deaths in 2010 to 2015.[1, 2] Within counties the difference is 1.12 (95% CI 1.07 to 1.16) years, and a separate regression in each of 267 metros finds lower life expectancy in more deprived tracts in every one. This chapter shows how those estimates are made and where they stop being informative. It covers the regression and its fixed effects, one regression per metro and per state, and a nine-measure index. It then takes up the change in a correlation with the size of the area, the split of variance between states, counties and tracts, and the suppressed cells of CDC WONDER. The gradient by decile of deprivation is the subject of Chapter 8.4, Place and the length of a life.

The slope is steeper within counties than across all tracts

The regression behind most figures in this book is a population-weighted least-squares line through tracts,

Y_i = \alpha + \beta\,\mathrm{ADI}_i + \varepsilon_i,

where $Y_i$ is the life expectancy of tract $i$ in years, $\mathrm{ADI}_i$ is its national percentile (higher is more deprived), $\alpha$ is the intercept, $\beta$ is the slope and $\varepsilon_i$ is the part of life expectancy the line leaves unexplained. The line minimizes the sum of squared residuals with each tract weighted by its 2019-2023 population, so a tract with few residents counts for less. The book reports the estimated $\beta$ with its sign reversed and scaled to a ten-point step. A reported slope is therefore the years of life expectancy lower for a ten-point step, and a slope above zero means life expectancy is lower where the ADI is higher. Counting each tract once moves none of the three slopes in Table 1 by more than 0.015 years, so the weights do not produce the result.

A national slope can come from composition, with the least deprived tracts concentrated in some states and the most deprived in others. The fixed-effects ladder tests for that by giving each state, and then each county, its own intercept,

Y_{ig} = \beta\,\mathrm{ADI}_{ig} + \mu_g + \varepsilon_{ig},

where $g$ indexes states in the second row of the figure and counties in the third, and $\mu_g$ is the constant of group $g$ . The slope $\beta$ then compares only tracts in the same state or county. The fits subtract each group's weighted mean from life expectancy and from the ADI before fitting, which gives the same slope as a dummy variable for every group. Errors cluster on the group that was absorbed, states for the first two rows and counties for the third. The intervals use the t distribution with one fewer degree of freedom than there are clusters.[3]

Across all tracts, life expectancy is 0.84 years lower per step, within states 0.92 and within counties 1.12, so the within-county slope is 1.3 times the national one. A composition story predicts the opposite order. The county fit uses 77,722 tracts, because a tract that is alone in its county adds no contrast.

Table 1. Life expectancy is 1.12 years lower per ten-point step of the ADI within counties, against 0.84 years across all tracts

A slope that survives these comparisons shows that rich and poor states at opposite ends of one line do not produce the pattern. It still compares tracts, and families sort themselves among tracts for reasons the regression does not observe, so it does not say what would happen to a family that moved.

Life expectancy is lower in more deprived tracts in every metro

A single slope assumes that the relationship is the same everywhere, and fitting the regression separately in each metro measures how much it differs. A metro here is a core-based statistical area, metropolitan or micropolitan, as the Census Bureau's county file codes it. Every tract takes the area of its county, which keeps Connecticut, whose planning regions stand in for counties. The file has no names, so Table 2 labels each metro by its most populous county, and Connecticut metros by a planning region. A metro enters if it has at least 40 tracts with a life expectancy and an ADI rank. 267 do, and they hold 82% of the residents in the sample.

A floor of 25 tracts gives 403 metros and a floor of 100 gives 134. Life expectancy is lower in more deprived tracts in every metro at each of the three floors.

The model is the first one, fitted within each metro $m$ with the same tract weights,

Y_{im} = \alpha_m + \beta_m\,\mathrm{ADI}_{im} + \varepsilon_{im},

where $\alpha_m$ and $\beta_m$ are the intercept and slope of metro $m$ . Errors cluster on counties in the 64 metros that have at least 5 counties. The rest use HC3 errors, which let the variance differ across tracts but treat tracts as independent, so their intervals may be too narrow where neighboring tracts resemble one another.

All 267 reported slopes are above zero, and the 95% interval of 260 of them lies above zero. The median metro has a life expectancy 1.20 (95% CI 1.14 to 1.23) years lower per ten-point step, with an interval from a bootstrap over metros.

At the 5th percentile of metros the difference is 0.70 years and at the 95th it is 1.67, a ratio of 2.4. Part of that range is estimation error. Let $\hat\beta_m$ be the estimated slope of metro $m$ and $v_m$ its squared standard error, let $s^2$ be the variance of the $\hat\beta_m$ across metros and let $\bar v$ be the mean of the $v_m$ . The variance of the true slopes is estimated by $\tau^2 = s^2 - \bar v$ , and each estimate can be shrunk toward the mean $\bar\beta$ in proportion to its reliability,

\tilde\beta_m = \bar\beta + \frac{\tau^2}{\tau^2 + v_m}\left(\hat\beta_m - \bar\beta\right).

Estimation error accounts for 48% of the variance of the estimated slopes. The standard deviation of the true slopes is 0.22 years against 0.31 for the estimates, and after shrinkage the 5th and 95th percentiles move to 0.88 and 1.46. The second tab of the figure sorts the estimates, which puts the metros with the least precise slopes at both ends. The order of neighbors therefore carries no information, and the chapter names no metro as the steepest or the flattest.

Table 2. Life expectancy is lower in more deprived tracts in all 267 metros, by a median of 1.20 years per ten-point step

Table 3 plots the metro slope against a metro's median household income, taken as the population-weighted median of its tracts' median incomes. Each correlation is computed weighted by metro residents and counting each metro once, with the slope measured as years lower per step. The correlation with income is +0.13 (95% CI −0.24 to 0.46) weighted and +0.05 (95% CI −0.11 to 0.19) unweighted.

Two further traits are the dispersion of tract ADI in the metro (the population-weighted standard deviation) and the metro's population. The correlation with ADI dispersion is −0.21 (95% CI −0.46 to 0.12) weighted and −0.14 (95% CI −0.27 to 0.01) unweighted, and with population −0.20 (95% CI −0.47 to 0.10) and −0.06 (95% CI −0.18 to 0.08). Each correlation has a bootstrap interval over metros, and all six intervals include zero, the one for ADI dispersion counting each metro once only just. No line is drawn through the cloud.

This is a failure to detect a relationship and not evidence that none exists. The slopes carry estimation error, 48% of their variance, which pulls every correlation toward zero. The weighted intervals are wide enough to leave a moderate correlation of either sign for income, dispersion or population. The traits also do not exhaust the candidates, which include health systems and housing, and the chapter tests no others.

Table 3. Metro slopes are not detectably related to metro median income, a correlation of +0.05 counting each metro once

The coefficient zoo returns coefficients of both signs

The method does not depend on life expectancy. For six behaviors and conditions in CDC PLACES, model-based estimates of the share of adults in each tract (modeled estimates),[4] the regression

P_i = a_s + \gamma_s\,\ln(\mathrm{income}_i) + \boldsymbol{\delta}_s'\,\mathbf{c}_i + \varepsilon_i

is fitted in each state $s$ across the tracts $i$ in it. Here $P_i$ is the share of adults with the condition or behavior, $\mathrm{income}_i$ is the tract's median household income, $\mathbf{c}_i$ holds the shares of residents who are ages 65 and over, Black alone and Hispanic, and the weight is the tract's adult population. The intercept $a_s$ , the income coefficient $\gamma_s$ and the control coefficients $\boldsymbol{\delta}_s$ are specific to state $s$ . The coefficient reported is $\gamma_s \ln 2$ , the change in points of prevalence for a doubling of income. A state enters if it has at least 60 tracts with every variable. All 51 states, counting the District of Columbia as one, do, which gives 306 coefficients. Chapter 7.1, Health behaviors and the supply of care reads what they say about behavior, and Table 4 shows the same dots.

The coefficients for smoking, obesity, diabetes, frequent mental distress and physical inactivity are negative in all 51 states, and the median for smoking is −4.7 points per doubling. The coefficient for binge drinking is positive in 50 states, with a median of +1.2 points.

The state coefficients can themselves be analyzed. States where smoking is more common have more negative smoking coefficients, a correlation of −0.73 (95% CI −0.84 to −0.60) across states. Most of that is scale, since the same proportional gradient is more points where the level is higher. Dividing each coefficient by the state's smoking level gives a correlation of −0.18 (95% CI −0.42 to 0.08).

Table 4. The same regression in every state gives coefficients below zero for five measures in all 51 states and above zero for binge drinking in 50

PLACES draws on the same census inputs as the income measure, and within a county it separates tracts only through their demographic mix. The coefficients therefore describe how the model maps composition into prevalence as well as how prevalence varies with income. A positive tract-level coefficient for binge drinking does not say who drinks.

A nine-measure index keeps one component and discards a second

The ADI is the deprivation measure behind this book's gradients, and Chapter 1.1, What the deprivation index measures examines how it is built. To study how an index of this kind behaves, this chapter builds a second one from nine tract measures in the American Community Survey (ACS) for 2019-2023. They are the poverty rate, the share of households on cash assistance or SNAP, the unemployment rate, the share of adults with a high school diploma or less, the share of households headed by a single parent, the log of median household income, the share of adults with a bachelor's degree or more, labor-force participation and homeownership. A tract needs all nine and an ADI rank, which leaves 82,545 tracts. Another 1,066 tracts with residents are dropped for lacking a measure or a rank. Each measure is standardized to mean zero and standard deviation one across tracts, each tract counted once as the ADI counts tracts, and the principal components are the eigenvectors of the correlation matrix,[5]

z_{ik} = \frac{x_{ik}-\bar x_k}{s_k}, \qquad R\,\ell_j = \lambda_j\,\ell_j, \qquad \mathrm{index}_i = \text{percentile rank of } \sum_{k}\ell_{1k}\,z_{ik},

where $x_{ik}$ is measure $k$ in tract $i$ , $\bar x_k$ and $s_k$ are its mean and standard deviation across tracts, $R$ is the correlation matrix, $\ell_j$ is the loading vector of component $j$ and $\lambda_j$ is its eigenvalue, the variance of the component in units of one standardized measure.

The first component holds 51.3% (95% CI 50.4% to 52.1%) of the variance and the second 16.0% (95% CI 15.5% to 16.4%), together 67.2% (95% CI 66.5% to 68.0%) because the shares are rounded separately. Weighting tracts by population gives 51.4% for the first. The Kaiser rule keeps components with an eigenvalue above one, and 2 clear it.[6] The rule is a convention with no statistical test behind it.

The measures overlap. The share of adults with a bachelor's degree and the share with a high school diploma or less correlate at −0.90 (95% CI −0.91 to −0.90). Cronbach's alpha for the nine, signed so that higher means more deprived, is 0.87 (95% CI 0.86 to 0.87).[7]

Table 5. The first of nine components holds 51.3% of the variance in nine census measures and the second holds 16.0%

The weights matter little. The index correlates with the score of a one-factor factor analysis at +0.994 (95% CI 0.993 to 0.994) and with an equal-weight average of the nine signed measures at +0.997 (95% CI 0.996 to 0.997), both rank correlations. Dropping any one measure and refitting moves the median tract by at most 2.3 percentile points, and the largest absolute loading on the first component is 0.41, so no measure dominates the score. The choice of measures matters more, since the ADI correlates with the index at +0.68 (95% CI 0.61 to 0.74).

The second component is orthogonal to the first by construction, so an index built on the first discards it. It contrasts homeownership (loading −0.59) with labor-force participation (+0.56) and the college share (+0.35), and tracts with the same index value can differ on it. That the nine measures cohere statistically does not show that the index measures what a family in the tract would call deprivation. Chapter 1.2, What a county average leaves out rebuilds a deprivation index for counties and Chapter 10.2, Families of places groups tracts instead of ranking them.

A correlation depends on the size of the area

The same data can give different correlations at different grains, a case of the modifiable areal unit problem.[8] Robinson showed in 1950 that a correlation across states can differ in size and sign from the correlation across the people inside them.[9, 10] Table 6 computes six pairs of tract measures across tracts, across counties and across states, all on the same 77,775 tracts, which lie in 3,112 counties and Connecticut planning regions. A county or state value is the population-weighted mean of its tracts' values. Each correlation is computed both weighted by the population of its unit and counting each unit once, with a bootstrap interval that draws counties for the tract correlations and states for the others. The state correlations rest on 51 states, so their intervals are wide.

Weighted by population, 4 of the 6 pairs are larger in absolute value across states than across tracts, and counting each unit once 4 are, but not the same pairs. College share and income go from +0.73 (95% CI 0.71 to 0.74) across tracts to +0.83 (95% CI 0.73 to 0.94) across states when weighted.

Homeownership and poverty fall toward zero under both weightings. Weighted, they go from −0.53 (95% CI −0.55 to −0.51) across tracts through −0.40 (95% CI −0.53 to −0.26) across counties to −0.05 (95% CI −0.30 to 0.17) across states. Counting each unit once they go from −0.52 (95% CI −0.54 to −0.50) to −0.11 (95% CI −0.32 to 0.11), so both state intervals include zero.

The weights decide the result for 2 of the 6 pairs. Unemployment and poverty rise from +0.43 (95% CI 0.41 to 0.46) across tracts to +0.59 (95% CI 0.52 to 0.66) across counties and fall to +0.33 (95% CI 0.08 to 0.59) across states when weighted. Counting each unit once they are +0.45 (95% CI 0.42 to 0.47), +0.53 (95% CI 0.44 to 0.61) and +0.50 (95% CI 0.32 to 0.65).

College share and life expectancy rise from +0.56 (95% CI 0.54 to 0.58) across tracts to +0.71 (95% CI 0.55 to 0.83) across states when weighted and fall from +0.55 (95% CI 0.53 to 0.57) to +0.48 (95% CI 0.13 to 0.81) counting each unit once. A weighted correlation across states gives populous states most of the weight, so the two weightings answer different questions. Averaging within an area removes the variation inside it, so a correlation of averages describes how the areas differ and says nothing about how the tracts or people inside them do.

Table 6. Weighted by population, the same pair of measures has a different correlation at each grain, and homeownership and poverty go from −0.53 across tracts to −0.05 across states

Most variation in life expectancy lies between tracts in the same county

Split the total variance of a tract measure $y$ , with population weights $w_i$ , into a part between states, a part between counties within states and a part between tracts within counties,

\underbrace{\sum_i w_i (y_i-\bar y)^2}_{\text{total}} = \underbrace{\sum_i w_i (\bar y_{s(i)}-\bar y)^2}_{\text{between states}} + \underbrace{\sum_i w_i (\bar y_{c(i)}-\bar y_{s(i)})^2}_{\text{between counties}} + \underbrace{\sum_i w_i (y_i-\bar y_{c(i)})^2}_{\text{between tracts}},

where $\bar y$ is the weighted mean of all tracts and $\bar y_{s(i)}$ and $\bar y_{c(i)}$ are the weighted means of the state and the county that contain tract $i$ . Each part over the total is a share of the variance, and the three shares sum to the whole. The split of life expectancy at birth uses the 78,008 tracts of 3,112 counties and Connecticut planning regions.

For life expectancy, the shares are 13.7% between states, 15.9% between counties in the same state and 70.3% between tracts in the same county. The split covers every tract in the sample and has no sampling interval, but it needs a caveat. The tract estimates are modeled, and their mean squared standard error equals 26.7% of the total variance, which sits inside the within-county share. Taking the published errors at face value, 60% of the variance that is not estimation error lies within counties. The errors of the 2020-tract file are mapping grade and understate the noise, so the share of the true differences that lies within counties is lower than that.

Table 7. 70.3 percent of the variance of tract life expectancy lies between tracts in the same county, 26.7 points of it estimation error, against 34% percent for the ADI

The ADI is the exception among the measures in the figure. Only 34% of its variance lies within counties and 37% lies between states, against 72% within counties for the nine-measure index. The two deprivation measures put their variation at different levels, and Chapter 1.1, What the deprivation index measures examines what the ADI is made of. A county map of life expectancy paints the county average and hides the largest share of its variance.

Large counties differ in how much of the ADI scale their tracts cover

A county's ADI range is the distance between the tract at the 10th percentile of ADI and the tract at the 90th, weighted by population. Across the 757 counties and Connecticut planning regions with at least 20 tracts that have an ADI rank, the median range is 39 percentile points.

The floor is a choice. With a floor of 10 tracts there are 1,319 counties and the median is 34 points. With a floor of 40 there are 421 and the median is 43.

Among the 30 most populous counties (Table 8), the range runs from 9 points in Santa Clara County, California, to 69 in Philadelphia County, Pennsylvania. Of the 30, 11 have a range of 25 points or less.

The median range in life expectancy between tracts at the 10th and 90th percentiles in those counties is 8.0 years. The counties are chosen by population and not by range, and tract life expectancy is modeled, so part of that spread in years is estimation error.

A county's mean ADI and its range are related, and how closely depends on the floor. Counting each county once, the correlation is −0.08 (95% CI −0.25 to 0.08) at a floor of 10 tracts, +0.27 (95% CI 0.06 to 0.44) at 20 and +0.62 (95% CI 0.45 to 0.74) at 40. Counting each county once, a county's mean says little about its range when small counties are included and more among large counties.

Weighted by county population the correlation is +0.47 (95% CI 0.16 to 0.63), +0.63 (95% CI 0.37 to 0.75) and +0.77 (95% CI 0.58 to 0.85) at the three floors. The figure shows the county averages as dots, and a county map shows only those and none of the whiskers.

Table 8. In each of the 30 most populous counties, tracts at the 10th and 90th percentile of the ADI are between 9 and 69 percentile points apart

Suppressed cells are not zeros

CDC WONDER withholds any cell with fewer than 10 events, so a suppressed cell holds at most 9 births, and a published zero is a real zero.

The natality file by county and birthweight class for 2018 to 2024 has 11,250 withheld cells of 48,552, or 23.2%, in the 578 counties that WONDER names in every year. Those are counties of at least 100,000 residents.

The share depends on the class (Table 9). It is 71.3% for births of 499 grams or less, 77.6% for births of 5,000 grams or more and 65.1% for births whose weight is not stated. The five middle classes, where counts are large, have 0 withheld cells.

Table 9. WONDER withholds 71.3% of county-year cells in the lightest birthweight class and 77.6% in the heaviest, against 0.0% in the five middle classes

In the lightest class 2,885 cells are withheld and 350 are published as zero. A withheld cell is never a zero, so reading the blanks as zeros would misstate each of those withheld cells, and in this class they outnumber the real zeros.

If every withheld cell held 9 births, the withheld cells of the lightest class would hold 60% of its births and those of the heaviest class 76%. That limit comes from the public threshold and the births in published cells, and it says nothing about any one cell. A count of births in a rare class summed over published cells covers only the county-years where the class is large enough to publish.

The book counts withheld cells, leaves them out of both numerator and denominator and derives no value for them. Gradients use no suppression floor, and a map of WONDER deaths needs at least 20 pooled deaths and says so. Chapter 2.1, Birthweight and preterm birth uses this file, and Chapter 10.3, Suppressed counties in death data takes up the same problem in death counts.

What these data cannot show

Every estimate in this chapter describes how an outcome is arranged across places. The slopes, the metro regressions, the index and the variance split compare tracts, counties and states, and none says what would happen to a person who moved or to a place that changed. A gradient of tract life expectancy on the ADI says nothing about individuals, which is the ecological inference problem. The steeper slope within counties is compatible with more than one explanation that the data cannot separate. Life expectancy is a modeled estimate for deaths in 2010 to 2015 that has not been updated. The 2020-tract file is area-weighted from 2010 tracts, so the estimate for a single tract is not reliable.

The metro slopes cover the residents of metros with enough tracts and say nothing about the rest. Their intervals use HC3 errors in most metros and may be too narrow, which would make the share of the spread that is estimation error larger. The ADI is a national percentile and understates deprivation in high-cost cities (Chapter 1.1, What the deprivation index measures), which bears on the metro slopes. Puerto Rico is not covered, and Connecticut's planning regions stand in for its counties in the metros, the variance split and the county ranges.

The index is internally coherent, and the data hold no independent measure of what residents would call deprivation, so they cannot show that it is valid. The ACS values behind the index, the ADI and the area-size correlations are survey estimates with sampling error that is largest in small tracts. The staged file carries no margins of error, so every analysis here treats them as exact. The ADI and the PLACES estimates draw on the same census inputs as the measures they are compared with, so part of each association is built in, and the data cannot say how much.

Weights can decide a result. The correlations across states for unemployment and poverty, and for the college share and life expectancy, change direction between the two weightings in the area-size section, and neither weighting is the correct one. The suppression figure counts withheld cells and cannot show what they hold. Counties below 100,000 residents are pooled by WONDER and do not appear as cells, and the bound on the share held in withheld cells is the largest value the threshold allows.

Tables

Table 1. Life expectancy is 1.12 years lower per ten-point step of the ADI within counties, against 0.84 years across all tracts

Slope of tract life expectancy at birth on the Area Deprivation Index national percentile, years lower per ten-point step, three specifications, deaths in 2010 to 2015

Specification	Weights	Years lower per ten points	Lower limit of the 95% interval	Upper limit of the 95% interval	Clusters	Tracts
All tracts	Population-weighted	0.84	0.75	0.92	51	77,936
All tracts	Each tract once	0.84	0.75	0.93	51	77,936
Within states	Population-weighted	0.92	0.78	1.05	51	77,936
Within states	Each tract once	0.91	0.77	1.05	51	77,936
Within counties	Population-weighted	1.12	1.07	1.16	2,890	77,722
Within counties	Each tract once	1.13	1.09	1.17	2,890	77,722

Note: Intervals use cluster-robust errors, clustered on states for the first two rows and on counties for the third, with t intervals on one fewer degree of freedom than there are clusters. The county fit leaves out tracts that are alone in their county, so it uses 77,722 of 77,936 tracts.

Note: Institutional tracts, such as prisons and dormitories, are dropped. USALEEP covers deaths in 2010 to 2015 and the 2020-tract file is area-weighted from 2010 tracts.

Source: USALEEP life expectancy, 2010-2015; Area Deprivation Index, 2023; ACS 2019-2023; population-weighted and unweighted regression slopes, modeled estimates

Data: vishalsingh.org/zipcode/report/gradients-slopes-and-variance/figures/fe_ladder.json

Table 2. Life expectancy is lower in more deprived tracts in all 267 metros, by a median of 1.20 years per ten-point step

One regression of tract life expectancy on the ADI national percentile in each metro with enough tracts, shown by metro and as a distribution, deaths in 2010 to 2015

Table 2.1. Distribution

After shrinkage the 5th and 95th percentile metro slopes move from 0.70 and 1.67 years to 0.88 and 1.46

Number of metros by slope of life expectancy on the ADI, years lower per ten-point step, estimated and shrunken, in bins of equal width

metros	series	x0	x1
1	Estimated slope	0	0.200
0	Estimated slope	0.200	0.400
3	Estimated slope	0.400	0.600
17	Estimated slope	0.600	0.800
48	Estimated slope	0.800	1
63	Estimated slope	1	1.20
76	Estimated slope	1.20	1.40
38	Estimated slope	1.40	1.60
14	Estimated slope	1.60	1.80
3	Estimated slope	1.80	2
2	Estimated slope	2	2.20
2	Estimated slope	2.20	2.40
0	Estimated slope	2.40	2.60
0	Estimated slope	2.60	2.80
0	Shrunken slope	0	0.200
0	Shrunken slope	0.200	0.400
1	Shrunken slope	0.400	0.600
3	Shrunken slope	0.600	0.800
30	Shrunken slope	0.800	1
104	Shrunken slope	1	1.20
103	Shrunken slope	1.20	1.40
25	Shrunken slope	1.40	1.60
1	Shrunken slope	1.60	1.80
0	Shrunken slope	1.80	2
0	Shrunken slope	2	2.20
0	Shrunken slope	2.20	2.40
0	Shrunken slope	2.40	2.60
0	Shrunken slope	2.60	2.80

Table 2.2. Slope by metro

Life expectancy is lower in more deprived tracts in every metro, from 0.70 years per ten-point step at the 5th percentile of metros to 1.67 at the 95th

Slope of tract life expectancy at birth on the ADI national percentile within each metro, years lower per ten-point step, one dot per metro labeled by its most populous county, deaths in 2010 to 2015

conf_high	conf_low	errors	estimate	metro	n
1.46	1.09	HC3 errors	1.27	Summit, OH	167
1.73	0.711	HC3 errors	1.22	Dougherty, GA	43
1.55	0.922	Clustered on counties	1.24	Albany, NY	232
1.17	0.770	HC3 errors	0.968	Bernalillo, NM	223
1.90	0.894	HC3 errors	1.39	Rapides, LA	42
1.13	0.802	HC3 errors	0.965	Lehigh, PA	188
1.30	0.637	Clustered on counties	0.970	Randall, TX	64
1.84	1.07	HC3 errors	1.45	Anchorage, AK	79
1.71	1.15	HC3 errors	1.43	Washtenaw, MI	86
1.63	0.507	HC3 errors	1.07	Outagamie, WI	52
0.934	0.363	HC3 errors	0.649	Buncombe, NC	112
1.59	0.881	HC3 errors	1.23	Clarke, GA	53
1.19	0.949	Clustered on counties	1.07	Fulton, GA	1,354
1.18	0.542	HC3 errors	0.859	Atlantic, NJ	69
1.06	−0.236	HC3 errors	0.411	Lee, AL	43
1.26	0.917	Clustered on counties	1.09	Richmond, GA	134
0.846	0.511	Clustered on counties	0.679	Travis, TX	442
1.16	0.751	HC3 errors	0.957	Kern, CA	212
1.67	1.14	Clustered on counties	1.40	Baltimore, MD	670
2.99	0.846	HC3 errors	1.92	Barnstable, MA	52
1.87	0.796	Clustered on counties	1.33	East Baton Rouge, LA	192
1.49	0.976	HC3 errors	1.23	Jefferson, TX	105
1.60	0.018	HC3 errors	0.807	Whatcom, WA	50
2.18	0.665	HC3 errors	1.42	Deschutes, OR	42
2.00	0.799	HC3 errors	1.40	Yellowstone, MT	42
1.88	0.700	HC3 errors	1.29	Broome, NY	64
1.45	1.17	Clustered on counties	1.31	Jefferson, AL	289
1.63	0.624	HC3 errors	1.13	McLean, IL	40
1.17	0.843	Clustered on counties	1.00	Ada, ID	190
1.80	0.798	Clustered on counties	1.30	Middlesex, MA	1,045
1.98	0.652	HC3 errors	1.31	Boulder, CO	64
2.98	1.76	HC3 errors	2.37	Kitsap, WA	53
1.28	0.983	HC3 errors	1.13	Western Connecticut, CT	212
1.73	1.00	HC3 errors	1.36	Cameron, TX	118
1.49	1.17	HC3 errors	1.33	Erie, NY	307
1.76	0.633	HC3 errors	1.20	Chittenden, VT	50
1.84	1.31	HC3 errors	1.58	Stark, OH	95
1.02	0.583	HC3 errors	0.799	Lee, FL	190
1.63	0.653	HC3 errors	1.14	Linn, IA	65
2.34	1.40	HC3 errors	1.87	Champaign, IL	43
1.29	0.654	Clustered on counties	0.972	Kanawha, WV	76
1.06	0.708	HC3 errors	0.884	Charleston, SC	159
1.28	0.986	Clustered on counties	1.13	Mecklenburg, NC	599
1.76	0.435	Clustered on counties	1.10	Albemarle, VA	51
1.60	1.01	Clustered on counties	1.31	Hamilton, TN	130
1.23	1.06	Clustered on counties	1.15	Cook, IL	2,183
2.11	1.14	HC3 errors	1.63	Butte, CA	51
1.69	1.45	Clustered on counties	1.57	Hamilton, OH	522
1.22	0.711	HC3 errors	0.964	Montgomery, TN	52
1.46	1.19	Clustered on counties	1.33	Cuyahoga, OH	589
1.16	0.549	HC3 errors	0.854	Brazos, TX	60
1.81	1.09	HC3 errors	1.45	El Paso, CO	155
1.46	0.677	HC3 errors	1.07	Boone, MO	40
1.33	0.942	Clustered on counties	1.14	Richland, SC	205
1.53	1.34	Clustered on counties	1.43	Muscogee, GA	87
1.74	0.972	Clustered on counties	1.36	Franklin, OH	474
1.18	0.518	HC3 errors	0.848	Nueces, TX	103
1.33	0.752	HC3 errors	1.04	Okaloosa, FL	63
0.986	0.834	Clustered on counties	0.910	Dallas, TX	1,501
1.36	0.726	HC3 errors	1.04	Baldwin, AL	41
1.51	0.487	HC3 errors	0.996	Scott, IA	107
1.87	1.45	HC3 errors	1.66	Montgomery, OH	209
1.57	0.953	HC3 errors	1.26	Volusia, FL	143
1.43	0.939	Clustered on counties	1.19	Denver, CO	635
1.38	0.681	Clustered on counties	1.03	Polk, IA	154
1.40	1.20	Clustered on counties	1.30	Wayne, MI	1,238
1.51	0.670	HC3 errors	1.09	St. Louis, MN	93
1.75	0.308	Clustered on counties	1.03	Durham, NC	131
1.79	−0.045	HC3 errors	0.871	Monroe, PA	46
2.17	1.36	HC3 errors	1.77	Elkhart, IN	44
0.851	0.416	HC3 errors	0.633	El Paso, TX	175
2.08	0.935	HC3 errors	1.50	Erie, PA	65
1.91	0.820	HC3 errors	1.36	Lane, OR	86
2.13	1.46	HC3 errors	1.80	Vanderburgh, IN	88
2.06	1.28	HC3 errors	1.67	Cass, ND	58
1.65	0.765	HC3 errors	1.21	Cumberland, NC	107
0.985	0.401	HC3 errors	0.693	Benton, AR	109
1.57	1.07	HC3 errors	1.32	Genesee, MI	124
1.62	0.646	HC3 errors	1.13	Florence, SC	52
2.02	0.669	HC3 errors	1.34	Lauderdale, AL	40
2.56	−0.146	HC3 errors	1.21	Larimer, CO	76
1.79	0.738	HC3 errors	1.27	Sebastian, AR	63
1.96	1.20	HC3 errors	1.58	Allen, IN	100
1.26	0.827	HC3 errors	1.04	Fresno, CA	218
1.77	1.21	HC3 errors	1.49	Alachua, FL	60
1.19	0.487	HC3 errors	0.840	Hall, GA	48
1.26	0.142	HC3 errors	0.701	Warren, NY	42
1.24	0.877	HC3 errors	1.06	Kent, MI	228
1.53	0.618	HC3 errors	1.07	Weld, CO	69
1.57	0.572	HC3 errors	1.07	Brown, WI	69
1.64	1.20	HC3 errors	1.42	Guilford, NC	169
1.36	0.988	HC3 errors	1.18	Greenville, SC	221
1.26	0.390	HC3 errors	0.827	Harrison, MS	137
1.98	1.13	HC3 errors	1.56	Washington, MD	65
1.55	0.981	HC3 errors	1.27	Dauphin, PA	126
1.29	0.997	HC3 errors	1.15	Capitol, CT	272
1.66	0.607	HC3 errors	1.13	Forrest, MS	45
1.36	0.395	HC3 errors	0.879	Catawba, NC	78
1.52	0.392	HC3 errors	0.955	Hawaii, HI	43
1.36	0.503	HC3 errors	0.930	Beaufort, SC	42
1.51	0.360	HC3 errors	0.936	Citrus, FL	41
1.13	0.623	HC3 errors	0.874	Terrebonne, LA	59
0.892	0.758	Clustered on counties	0.825	Harris, TX	1,457
2.12	0.348	Clustered on counties	1.24	Cabell, WV	102
1.53	1.08	HC3 errors	1.30	Madison, AL	108
1.48	1.18	Clustered on counties	1.33	Marion, IN	453
1.59	0.656	HC3 errors	1.12	Jackson, MI	45
1.50	1.00	Clustered on counties	1.25	Hinds, MS	149
1.83	0.819	HC3 errors	1.32	Madison, TN	50
1.53	1.23	Clustered on counties	1.38	Duval, FL	319
1.47	0.562	HC3 errors	1.02	Washington, TN	53
2.26	0.816	HC3 errors	1.54	Jasper, MO	42
2.53	0.560	HC3 errors	1.54	Maui, HI	42
1.55	0.897	HC3 errors	1.22	Kalamazoo, MI	63
1.50	1.05	Clustered on counties	1.27	Jackson, MO	556
1.21	0.656	HC3 errors	0.934	Benton, WA	63
1.00	0.383	HC3 errors	0.694	Bell, TX	91
2.04	0.991	Clustered on counties	1.51	Sullivan, TN	77
1.88	0.315	HC3 errors	1.10	Ulster, NY	41
1.59	1.29	Clustered on counties	1.44	Knox, TN	198
1.44	0.826	Clustered on counties	1.13	Lafayette, LA	125
1.98	0.909	HC3 errors	1.44	Tippecanoe, IN	49
1.48	0.741	HC3 errors	1.11	Calcasieu, LA	52
1.29	0.490	HC3 errors	0.892	Mohave, AZ	55
1.51	0.881	HC3 errors	1.20	Polk, FL	162
1.57	0.750	HC3 errors	1.16	Lancaster, PA	112
1.33	0.912	HC3 errors	1.12	Ingham, MI	135
1.34	0.641	HC3 errors	0.991	Webb, TX	61
0.970	−0.013	HC3 errors	0.478	Doña Ana, NM	57
1.03	0.735	HC3 errors	0.885	Clark, NV	475
1.53	0.543	HC3 errors	1.03	Grafton, NH	66
1.84	0.989	Clustered on counties	1.41	Fayette, KY	124
1.65	0.994	HC3 errors	1.32	Lancaster, NE	74
1.82	0.949	Clustered on counties	1.38	Pulaski, AR	177
1.98	0.429	HC3 errors	1.20	Laurel, KY	43
1.02	0.470	HC3 errors	0.744	Gregg, TX	64
1.45	1.15	HC3 errors	1.30	Los Angeles, CA	2,923
1.73	1.29	Clustered on counties	1.51	Jefferson, KY	320
1.25	0.097	HC3 errors	0.675	Lubbock, TX	97
1.90	0.911	Clustered on counties	1.40	Bedford, VA	64
1.68	1.12	Clustered on counties	1.40	Bibb, GA	64
1.15	0.419	HC3 errors	0.782	Dane, WI	123
1.55	0.681	HC3 errors	1.12	Hillsborough, NH	99
0.888	0.374	HC3 errors	0.631	Hidalgo, TX	208
1.83	0.570	HC3 errors	1.20	Jackson, OR	51
1.45	1.12	Clustered on counties	1.29	Shelby, TN	316
1.32	0.358	HC3 errors	0.839	Merced, CA	60
0.922	0.725	HC3 errors	0.823	Miami-Dade, FL	1,377
1.32	1.08	HC3 errors	1.20	Milwaukee, WI	374
1.52	0.938	Clustered on counties	1.23	Hennepin, MN	847
1.61	1.02	HC3 errors	1.31	Mobile, AL	128
1.35	0.694	HC3 errors	1.02	Stanislaus, CA	111
1.93	1.04	HC3 errors	1.49	Ouachita, LA	57
2.14	1.11	HC3 errors	1.62	Monroe, MI	40
1.49	0.964	HC3 errors	1.23	Montgomery, AL	94
1.98	0.493	HC3 errors	1.24	Skagit, WA	41
1.92	1.19	HC3 errors	1.56	Muskegon, MI	44
1.15	0.593	HC3 errors	0.872	Horry, SC	110
1.38	0.423	HC3 errors	0.902	Collier, FL	94
1.55	0.985	Clustered on counties	1.27	Davidson, TN	384
1.31	0.818	HC3 errors	1.06	South Central Connecticut, CT	128
1.38	0.948	Clustered on counties	1.16	Jefferson, LA	379
1.12	0.421	Clustered on counties	0.771	Kings, NY	4,563
1.92	1.12	HC3 errors	1.52	Berrien, MI	51
1.19	0.714	HC3 errors	0.953	Sarasota, FL	204
1.66	0.818	HC3 errors	1.24	Southeastern Connecticut, CT	62
1.27	0.541	HC3 errors	0.906	Marion, FL	77
1.96	1.32	HC3 errors	1.64	Davis, UT	134
1.49	1.11	Clustered on counties	1.30	Oklahoma, OK	380
2.18	1.21	HC3 errors	1.69	Thurston, WA	60
1.31	0.980	Clustered on counties	1.14	Douglas, NE	247
1.03	0.689	HC3 errors	0.859	Orange, FL	460
1.49	0.593	HC3 errors	1.04	Ventura, CA	182
1.47	1.01	HC3 errors	1.24	Brevard, FL	140
1.23	0.379	HC3 errors	0.803	Bay, FL	46
1.73	1.17	HC3 errors	1.45	Escambia, FL	106
1.41	1.07	Clustered on counties	1.24	Peoria, IL	104
1.27	1.13	Clustered on counties	1.20	Philadelphia, PA	1,455
0.989	0.800	HC3 errors	0.894	Maricopa, AZ	948
1.21	0.893	Clustered on counties	1.05	Allegheny, PA	674
1.45	0.677	HC3 errors	1.06	Cumberland, ME	127
1.51	1.21	Clustered on counties	1.36	Multnomah, OR	537
1.67	1.00	HC3 errors	1.34	St. Lucie, FL	102
2.22	1.04	HC3 errors	1.63	Schuylkill, PA	40
1.02	0.313	HC3 errors	0.666	Orange, NY	162
1.26	0.223	HC3 errors	0.740	Yavapai, AZ	70
1.46	1.07	Clustered on counties	1.26	Providence, RI	357
1.53	0.768	HC3 errors	1.15	Utah, UT	140
1.79	1.14	HC3 errors	1.47	Pueblo, CO	55
1.58	0.643	HC3 errors	1.11	Charlotte, FL	40
1.42	0.528	HC3 errors	0.976	Racine, WI	41
1.04	0.745	HC3 errors	0.890	Wake, NC	268
1.39	0.967	HC3 errors	1.18	Berks, PA	103
1.73	0.732	HC3 errors	1.23	Shasta, CA	49
1.67	0.812	HC3 errors	1.24	Washoe, NV	117
1.36	1.12	Clustered on counties	1.24	Chesterfield, VA	298
1.15	0.890	HC3 errors	1.02	Riverside, CA	939
1.82	0.799	Clustered on counties	1.31	Roanoke, VA	75
1.41	0.373	HC3 errors	0.892	Olmsted, MN	52
1.39	1.05	Clustered on counties	1.22	Monroe, NY	288
1.91	1.01	HC3 errors	1.46	Winnebago, IL	88
1.61	1.27	HC3 errors	1.44	Sacramento, CA	519
2.09	1.35	HC3 errors	1.72	Saginaw, MI	59
1.86	0.503	HC3 errors	1.18	Stearns, MN	43
1.43	1.07	Clustered on counties	1.25	St. Louis, MO	669
2.15	0.667	HC3 errors	1.41	Marion, OR	78
1.58	0.320	HC3 errors	0.950	Monterey, CA	96
1.21	0.759	HC3 errors	0.986	Sussex, DE	105
1.51	0.894	HC3 errors	1.20	Salt Lake, UT	247
1.05	0.647	Clustered on counties	0.849	Bexar, TX	453
1.34	0.826	HC3 errors	1.08	San Diego, CA	681
3.04	1.67	Clustered on counties	2.36	Alameda, CA	1,044
1.09	0.161	HC3 errors	0.624	Santa Clara, CA	405
1.48	−0.040	HC3 errors	0.721	San Luis Obispo, CA	61
1.73	−0.548	HC3 errors	0.592	Santa Cruz, CA	63
1.03	0.374	HC3 errors	0.703	Santa Fe, NM	52
1.64	0.787	HC3 errors	1.22	Santa Barbara, CA	98
2.12	0.547	HC3 errors	1.34	Sonoma, CA	116
1.28	0.806	HC3 errors	1.04	Chatham, GA	101
1.64	1.08	HC3 errors	1.36	Luzerne, PA	162
1.79	1.34	HC3 errors	1.56	King, WA	829
1.27	0.555	HC3 errors	0.914	Indian River, FL	41
1.51	1.01	HC3 errors	1.26	Caddo, LA	114
1.87	0.709	HC3 errors	1.29	Minnehaha, SD	60
1.51	1.05	HC3 errors	1.28	St. Joseph, IN	92
1.63	0.905	HC3 errors	1.27	Spartanburg, SC	85
1.84	1.26	HC3 errors	1.55	Spokane, WA	140
2.00	1.14	HC3 errors	1.57	Sangamon, IL	56
1.67	1.27	HC3 errors	1.47	Hampden, MA	151
2.08	0.848	Clustered on counties	1.46	Greene, MO	111
2.70	1.57	HC3 errors	2.13	Clark, OH	41
1.76	1.15	HC3 errors	1.46	San Joaquin, CA	169
1.43	0.879	HC3 errors	1.15	Onondaga, NY	183
1.22	0.803	HC3 errors	1.01	Leon, FL	82
1.10	0.888	HC3 errors	0.995	Hillsborough, FL	692
2.40	1.28	Clustered on counties	1.84	Vigo, IN	53
1.64	0.254	HC3 errors	0.949	Bowie, TX	42
1.42	1.05	HC3 errors	1.23	Lucas, OH	213
1.32	1.12	Clustered on counties	1.22	Shawnee, KS	59
1.42	0.785	HC3 errors	1.10	Grand Traverse, MI	43
1.58	1.11	HC3 errors	1.34	Mercer, NJ	79
1.30	0.873	HC3 errors	1.08	Pima, AZ	246
1.48	0.994	Clustered on counties	1.24	Tulsa, OK	304
1.84	0.561	HC3 errors	1.20	Lee, MS	40
1.28	0.561	HC3 errors	0.920	Tuscaloosa, AL	69
1.45	0.295	HC3 errors	0.875	Smith, TX	53
1.57	0.764	HC3 errors	1.16	Honolulu, HI	286
2.16	1.08	HC3 errors	1.62	Oneida, NY	83
2.86	1.47	HC3 errors	2.16	Solano, CA	96
1.68	1.04	Clustered on counties	1.36	Virginia Beach, VA	436
1.44	0.731	HC3 errors	1.08	Tulare, CA	101
1.71	0.963	HC3 errors	1.34	McLennan, TX	55
1.37	0.235	HC3 errors	0.804	Houston, GA	44
1.34	0.861	Clustered on counties	1.10	Fairfax, VA	1,350
1.34	0.879	HC3 errors	1.11	Naugatuck Valley, CT	95
1.67	0.512	HC3 errors	1.09	Black Hawk, IA	46
2.51	0.881	HC3 errors	1.69	Marathon, WI	41
2.19	1.04	HC3 errors	1.61	Belmont, OH	48
1.51	1.07	HC3 errors	1.29	Sedgwick, KS	164
2.15	0.833	HC3 errors	1.49	Wichita, TX	44
1.27	0.688	HC3 errors	0.978	New Hanover, NC	61
1.59	0.891	Clustered on counties	1.24	Forsyth, NC	161
1.24	0.783	HC3 errors	1.01	Worcester, MA	184
1.41	0.812	HC3 errors	1.11	Yakima, WA	56
1.75	1.10	HC3 errors	1.43	York, PA	105
2.14	1.27	HC3 errors	1.70	Mahoning, OH	155
0.485	−0.212	HC3 errors	0.137	Yuma, AZ	60

Source: USALEEP life expectancy, 2010-2015; Area Deprivation Index, 2023; ACS 2019-2023; population-weighted regression slopes, modeled estimates

Data: vishalsingh.org/zipcode/report/gradients-slopes-and-variance/figures/metro_slopes_forest.json

Table 3. Metro slopes are not detectably related to metro median income, a correlation of +0.05 counting each metro once

Slope of life expectancy on the ADI, years lower per ten-point step, against the population-weighted median of tract median household income, one dot per metro, dot area is residents, ACS 2019-2023

Metro (most populous county)	Median household income (dollars)	Years lower per ten points	Residents
Summit, OH	78,539	1.27	669,646
Dougherty, GA	58,616	1.22	137,062
Albany, NY	90,562	1.24	836,414
Bernalillo, NM	67,143	0.97	893,158
Rapides, LA	55,364	1.40	140,716
Lehigh, PA	84,487	0.96	833,485
Randall, TX	68,494	0.97	237,500
Anchorage, AK	98,834	1.45	380,453
Washtenaw, MI	96,960	1.43	304,946
Outagamie, WI	79,222	1.07	219,811
Buncombe, NC	68,432	0.65	441,184
Clarke, GA	69,709	1.23	199,046
Fulton, GA	87,864	1.07	5,630,116
Atlantic, NJ	82,927	0.86	268,098
Lee, AL	70,429	0.41	164,528
Richmond, GA	69,885	1.09	574,113
Travis, TX	100,981	0.68	2,125,441
Kern, CA	65,661	0.96	825,064
Baltimore, MD	105,865	1.40	2,720,602
Barnstable, MA	95,323	1.92	216,294
East Baton Rouge, LA	72,773	1.33	773,655
Jefferson, TX	69,211	1.23	370,574
Whatcom, WA	86,204	0.81	209,575
Deschutes, OR	87,928	1.42	186,931
Yellowstone, MT	74,303	1.40	180,017
Broome, NY	69,856	1.29	229,131
Jefferson, AL	73,171	1.31	1,073,001
McLean, IL	93,924	1.13	146,591
Ada, ID	82,830	1.00	770,927
Middlesex, MA	115,783	1.30	4,655,005
Boulder, CO	111,280	1.31	269,739
Kitsap, WA	96,978	2.37	254,129
Western Connecticut, CT	119,204	1.13	906,110
Cameron, TX	49,397	1.36	420,423
Erie, NY	74,430	1.33	1,114,712
Chittenden, VT	97,573	1.20	212,127
Stark, OH	67,916	1.58	393,729
Lee, FL	73,765	0.80	706,110
Linn, IA	79,604	1.14	267,683
Champaign, IL	78,227	1.87	189,134
Kanawha, WV	56,842	0.97	240,764
Charleston, SC	84,750	0.88	755,397
Mecklenburg, NC	79,411	1.13	2,441,459
Albemarle, VA	99,461	1.10	205,622
Hamilton, TN	66,386	1.31	550,749
Cook, IL	89,535	1.15	9,078,307
Butte, CA	66,437	1.63	199,170
Hamilton, OH	83,974	1.56	2,187,695
Montgomery, TN	69,185	0.96	216,994
Cuyahoga, OH	75,421	1.32	1,974,832
Brazos, TX	64,066	0.85	215,331
El Paso, CO	90,786	1.45	683,216
Boone, MO	75,882	1.07	177,863
Richland, SC	68,108	1.14	774,726
Muscogee, GA	56,087	1.44	307,364
Franklin, OH	84,228	1.36	1,996,447
Nueces, TX	62,659	0.85	400,651
Okaloosa, FL	81,407	1.04	282,957
Dallas, TX	89,919	0.91	6,987,097
Baldwin, AL	72,143	1.04	233,001
Scott, IA	73,210	1.00	371,800
Montgomery, OH	71,585	1.66	767,209
Volusia, FL	68,729	1.26	654,841
Denver, CO	103,222	1.19	2,715,379
Polk, IA	88,621	1.03	699,409
Wayne, MI	78,215	1.30	4,220,000
St. Louis, MN	73,474	1.09	269,181
Durham, NC	80,667	1.03	577,271
Monroe, PA	82,889	0.87	150,264
Elkhart, IN	74,014	1.77	202,494
El Paso, TX	59,731	0.63	815,804
Erie, PA	68,900	1.50	241,679
Lane, OR	75,900	1.36	356,564
Vanderburgh, IN	70,479	1.80	307,015
Cass, ND	81,328	1.67	245,120
Cumberland, NC	63,983	1.21	481,759
Benton, AR	79,398	0.69	532,009
Genesee, MI	67,173	1.32	386,955
Florence, SC	51,228	1.13	194,799
Lauderdale, AL	62,256	1.34	150,584
Larimer, CO	96,086	1.21	335,193
Sebastian, AR	57,749	1.27	246,136
Allen, IN	74,450	1.58	419,042
Fresno, CA	68,789	1.04	972,223
Alachua, FL	65,453	1.49	274,469
Hall, GA	78,409	0.84	197,646
Warren, NY	73,926	0.70	120,895
Kent, MI	79,418	1.06	1,042,581
Weld, CO	91,034	1.07	303,989
Brown, WI	78,555	1.07	314,505
Guilford, NC	64,720	1.42	734,908
Greenville, SC	67,109	1.18	907,427
Harrison, MS	63,333	0.83	403,294
Washington, MD	81,677	1.56	285,382
Dauphin, PA	80,314	1.27	577,920
Capitol, CT	101,384	1.15	1,084,341
Forrest, MS	57,582	1.13	168,319
Catawba, NC	60,922	0.88	349,670
Hawaii, HI	82,193	0.95	203,684
Beaufort, SC	80,222	0.93	208,704
Citrus, FL	52,582	0.94	158,693
Terrebonne, LA	66,584	0.87	201,316
Harris, TX	82,729	0.82	6,642,463
Cabell, WV	59,798	1.24	342,756
Madison, AL	90,881	1.30	458,766
Marion, IN	82,707	1.33	2,032,367
Jackson, MI	73,094	1.12	153,400
Hinds, MS	63,404	1.25	552,763
Madison, TN	60,270	1.32	173,169
Duval, FL	79,227	1.38	1,564,210
Washington, TN	55,932	1.02	207,033
Jasper, MO	64,575	1.54	183,022
Maui, HI	99,412	1.54	159,226
Kalamazoo, MI	76,432	1.22	245,459
Jackson, MO	84,494	1.27	2,096,382
Benton, WA	84,220	0.93	299,764
Bell, TX	69,611	0.69	405,407
Sullivan, TN	55,583	1.51	300,116
Ulster, NY	84,187	1.10	142,011
Knox, TN	72,138	1.44	809,385
Lafayette, LA	66,671	1.13	448,515
Tippecanoe, IN	66,937	1.44	195,160
Calcasieu, LA	66,533	1.11	202,151
Mohave, AZ	53,143	0.89	184,833
Polk, FL	65,270	1.20	714,860
Lancaster, PA	85,303	1.16	537,447
Ingham, MI	73,858	1.12	491,538
Webb, TX	56,078	0.99	245,692
Doña Ana, NM	52,415	0.48	210,924
Clark, NV	76,382	0.89	2,040,360
Grafton, NH	80,713	1.03	214,011
Fayette, KY	74,213	1.41	469,966
Lancaster, NE	78,349	1.32	309,128
Pulaski, AR	66,246	1.39	705,921
Laurel, KY	48,033	1.20	135,185
Gregg, TX	66,104	0.74	264,952
Los Angeles, CA	93,203	1.30	12,424,893
Jefferson, KY	75,097	1.51	1,237,206
Lubbock, TX	67,469	0.68	281,286
Bedford, VA	68,254	1.40	246,474
Bibb, GA	63,730	1.40	218,037
Dane, WI	95,609	0.78	572,839
Hillsborough, NH	111,157	1.12	406,867
Hidalgo, TX	51,427	0.63	862,862
Jackson, OR	69,562	1.20	219,490
Shelby, TN	66,331	1.29	1,235,221
Merced, CA	62,533	0.84	270,880
Miami-Dade, FL	74,414	0.82	5,791,746
Milwaukee, WI	82,816	1.20	1,378,567
Hennepin, MN	100,713	1.23	3,551,467
Mobile, AL	60,264	1.31	406,513
Stanislaus, CA	79,423	1.02	548,836
Ouachita, LA	53,701	1.49	194,368
Monroe, MI	73,561	1.62	155,001
Montgomery, AL	63,880	1.23	333,773
Skagit, WA	84,454	1.24	130,407
Muskegon, MI	65,723	1.56	172,022
Horry, SC	67,828	0.87	489,212
Collier, FL	86,133	0.90	346,920
Davidson, TN	83,658	1.27	1,838,601
South Central Connecticut, CT	93,307	1.06	534,415
Jefferson, LA	67,691	1.16	1,171,586
Kings, NY	100,747	0.77	18,857,008
Berrien, MI	68,611	1.52	146,726
Sarasota, FL	80,341	0.95	831,108
Southeastern Connecticut, CT	94,811	1.24	260,834
Marion, FL	61,597	0.91	333,673
Davis, UT	100,390	1.64	684,276
Oklahoma, OK	73,307	1.30	1,362,954
Thurston, WA	97,796	1.69	289,042
Douglas, NE	84,841	1.14	906,982
Orange, FL	78,256	0.86	2,563,054
Ventura, CA	104,381	1.04	814,047
Brevard, FL	77,672	1.24	605,284
Bay, FL	74,261	0.80	173,076
Escambia, FL	75,748	1.45	475,092
Peoria, IL	71,097	1.24	389,448
Philadelphia, PA	95,391	1.20	5,987,126
Maricopa, AZ	86,916	0.89	4,383,220
Allegheny, PA	76,103	1.05	2,255,125
Cumberland, ME	90,084	1.06	484,107
Multnomah, OR	96,267	1.36	2,415,437
St. Lucie, FL	73,760	1.33	492,192
Schuylkill, PA	66,594	1.63	134,044
Orange, NY	100,646	0.67	654,415
Yavapai, AZ	65,085	0.74	239,019
Providence, RI	89,419	1.26	1,583,138
Utah, UT	101,864	1.15	622,944
Pueblo, CO	67,283	1.47	165,819
Charlotte, FL	63,346	1.11	166,856
Racine, WI	79,997	0.98	176,535
Wake, NC	94,676	0.89	1,312,735
Berks, PA	84,157	1.18	415,678
Shasta, CA	73,870	1.23	177,622
Washoe, NV	91,819	1.24	436,487
Chesterfield, VA	87,203	1.24	1,220,266
Riverside, CA	86,902	1.02	4,445,399
Roanoke, VA	68,774	1.31	304,511
Olmsted, MN	90,266	0.89	227,252
Monroe, NY	75,298	1.22	1,017,611
Winnebago, IL	72,373	1.46	333,345
Sacramento, CA	95,171	1.44	2,275,221
Saginaw, MI	62,664	1.72	178,008
Stearns, MN	78,277	1.18	191,054
St. Louis, MO	80,401	1.25	2,698,897
Marion, OR	80,970	1.41	420,236
Monterey, CA	88,897	0.95	412,418
Sussex, DE	76,838	0.99	409,522
Salt Lake, UT	98,169	1.20	1,180,115
Bexar, TX	73,970	0.85	2,362,483
San Diego, CA	105,089	1.08	3,038,849
Alameda, CA	139,249	2.35	4,509,425
Santa Clara, CA	161,876	0.62	1,901,120
San Luis Obispo, CA	96,635	0.72	245,057
Santa Cruz, CA	119,621	0.59	237,745
Santa Fe, NM	73,619	0.70	148,272
Santa Barbara, CA	99,372	1.22	412,638
Sonoma, CA	102,512	1.34	464,875
Chatham, GA	80,415	1.04	386,024
Luzerne, PA	64,824	1.36	547,160
King, WA	116,777	1.56	3,901,567
Indian River, FL	69,670	0.91	159,904
Caddo, LA	58,198	1.26	387,561
Minnehaha, SD	81,909	1.29	259,327
St. Joseph, IN	66,265	1.28	306,731
Spartanburg, SC	68,253	1.26	332,585
Spokane, WA	73,703	1.55	571,186
Sangamon, IL	81,653	1.57	206,373
Hampden, MA	78,217	1.47	661,944
Greene, MO	67,133	1.46	460,198
Clark, OH	60,631	2.13	124,716
San Joaquin, CA	93,350	1.46	765,093
Onondaga, NY	78,968	1.15	571,929
Leon, FL	73,029	1.01	313,008
Hillsborough, FL	72,580	0.99	2,915,720
Vigo, IN	62,079	1.84	173,868
Bowie, TX	56,942	0.95	132,963
Lucas, OH	69,543	1.23	618,783
Shawnee, KS	72,654	1.22	230,593
Grand Traverse, MI	80,819	1.10	152,857
Mercer, NJ	99,710	1.34	365,389
Pima, AZ	68,805	1.08	970,791
Tulsa, OK	67,647	1.24	992,751
Lee, MS	60,211	1.20	154,901
Tuscaloosa, AL	66,848	0.92	245,890
Smith, TX	72,525	0.88	229,377
Honolulu, HI	114,427	1.17	920,726
Oneida, NY	73,753	1.62	269,625
Solano, CA	100,671	2.16	438,687
Virginia Beach, VA	82,992	1.36	1,641,154
Tulare, CA	67,494	1.08	471,294
McLennan, TX	64,453	1.34	256,286
Houston, GA	83,283	0.80	189,105
Fairfax, VA	129,801	1.10	5,859,377
Naugatuck Valley, CT	93,202	1.11	434,486
Black Hawk, IA	75,013	1.09	155,456
Marathon, WI	77,127	1.69	163,997
Belmont, OH	60,282	1.61	129,839
Sedgwick, KS	71,185	1.29	639,062
Wichita, TX	65,991	1.49	129,389
New Hanover, NC	74,780	0.98	269,411
Forsyth, NC	66,974	1.24	653,947
Worcester, MA	99,647	1.01	840,052
Yakima, WA	68,633	1.11	253,792
York, PA	84,907	1.43	444,752
Mahoning, OH	58,912	1.70	512,839
Yuma, AZ	59,195	0.14	190,651

Note: The income of a metro is the population-weighted median of its tracts' median household incomes, a proxy built from tract values. Slopes are estimates with the intervals shown in the metro figure, which this plot does not draw.

Note: The correlations in the text are weighted by metro residents and counting each metro once, with bootstrap intervals over metros; all six intervals include zero.

Source: USALEEP life expectancy, 2010-2015; ACS 2019-2023; Area Deprivation Index, 2023; metro slopes, population-weighted within metros, modeled estimates

Data: vishalsingh.org/zipcode/report/gradients-slopes-and-variance/figures/slope_heterogeneity.json

Table 4. The same regression in every state gives coefficients below zero for five measures in all 51 states and above zero for binge drinking in 50

Points of prevalence per doubling of tract median household income, one dot per state and measure, adults, with controls for the age, Black and Hispanic shares of residents, 2022

Measure	State	Points per doubling	Lower limit of the 95% interval	Upper limit of the 95% interval	Tracts
Current smoking	Alabama	−5.29	−6.54	−4.05	1,423
Current smoking	Alaska	−8.13	−10.80	−5.45	173
Current smoking	Arizona	−5.12	−7.05	−3.18	1,725
Current smoking	Arkansas	−6.01	−7.17	−4.85	818
Current smoking	California	−3.66	−4.10	−3.23	9,003
Current smoking	Colorado	−2.88	−3.61	−2.15	1,423
Current smoking	Connecticut	−3.76	−4.87	−2.65	870
Current smoking	Delaware	−3.63	−5.14	−2.13	256
Current smoking	District of Columbia	−2.38	−3.45	−1.32	201
Current smoking	Florida	−4.95	−5.54	−4.36	5,053
Current smoking	Georgia	−5.70	−6.27	−5.14	2,766
Current smoking	Hawaii	−3.96	−4.52	−3.40	419
Current smoking	Idaho	−3.30	−4.00	−2.59	453
Current smoking	Illinois	−4.67	−5.93	−3.42	3,239
Current smoking	Indiana	−5.07	−6.55	−3.60	1,678
Current smoking	Iowa	−4.47	−5.64	−3.31	890
Current smoking	Kansas	−5.16	−6.46	−3.87	811
Current smoking	Kentucky	−7.14	−7.89	−6.38	1,287
Current smoking	Louisiana	−6.01	−6.60	−5.43	1,352
Current smoking	Maine	−5.80	−6.68	−4.91	399
Current smoking	Maryland	−4.84	−5.75	−3.92	1,449
Current smoking	Massachusetts	−3.95	−5.70	−2.20	1,585
Current smoking	Michigan	−5.85	−6.69	−5.01	2,869
Current smoking	Minnesota	−4.56	−5.34	−3.78	1,497
Current smoking	Mississippi	−5.77	−7.25	−4.29	865
Current smoking	Missouri	−6.34	−7.36	−5.31	1,644
Current smoking	Montana	−6.65	−8.29	−5.00	315
Current smoking	Nebraska	−3.80	−4.23	−3.37	550
Current smoking	Nevada	−4.54	−5.64	−3.44	767
Current smoking	New Hampshire	−4.38	−5.14	−3.62	347
Current smoking	New Jersey	−4.31	−4.87	−3.75	2,154
Current smoking	New Mexico	−6.93	−8.55	−5.31	594
Current smoking	New York	−4.70	−5.23	−4.17	5,182
Current smoking	North Carolina	−5.40	−6.02	−4.78	2,628
Current smoking	North Dakota	−4.04	−6.23	−1.86	227
Current smoking	Ohio	−6.77	−7.24	−6.30	3,129
Current smoking	Oklahoma	−6.30	−7.23	−5.36	1,189
Current smoking	Oregon	−4.57	−5.48	−3.66	991
Current smoking	Pennsylvania	−5.51	−5.98	−5.05	3,377
Current smoking	Rhode Island	−4.53	−5.11	−3.94	246
Current smoking	South Carolina	−5.61	−6.44	−4.78	1,301
Current smoking	South Dakota	−7.73	−10.72	−4.74	242
Current smoking	Tennessee	−6.64	−7.92	−5.36	1,675
Current smoking	Texas	−4.35	−4.98	−3.73	6,802
Current smoking	Utah	−1.76	−2.66	−0.86	707
Current smoking	Vermont	−3.95	−5.08	−2.83	192
Current smoking	Virginia	−4.66	−5.25	−4.07	2,152
Current smoking	Washington	−3.93	−4.54	−3.32	1,767
Current smoking	West Virginia	−5.86	−7.73	−3.99	543
Current smoking	Wisconsin	−4.04	−4.66	−3.42	1,523
Current smoking	Wyoming	−2.44	−5.45	0.56	159
Obesity	Alabama	−4.19	−5.38	−3.00	1,423
Obesity	Alaska	−5.44	−6.79	−4.08	173
Obesity	Arizona	−2.94	−4.29	−1.60	1,725
Obesity	Arkansas	−4.27	−5.48	−3.06	818
Obesity	California	−4.02	−5.36	−2.69	9,003
Obesity	Colorado	−1.90	−3.21	−0.58	1,423
Obesity	Connecticut	−5.18	−7.75	−2.61	870
Obesity	Delaware	−3.57	−4.88	−2.26	256
Obesity	District of Columbia	−1.46	−2.58	−0.33	201
Obesity	Florida	−5.09	−5.94	−4.24	5,053
Obesity	Georgia	−6.05	−7.26	−4.83	2,766
Obesity	Hawaii	−3.12	−3.40	−2.84	419
Obesity	Idaho	−3.29	−4.95	−1.62	453
Obesity	Illinois	−4.31	−5.85	−2.77	3,239
Obesity	Indiana	−3.79	−5.61	−1.96	1,678
Obesity	Iowa	−3.37	−4.61	−2.12	890
Obesity	Kansas	−4.54	−5.89	−3.19	811
Obesity	Kentucky	−5.33	−6.31	−4.34	1,287
Obesity	Louisiana	−5.19	−6.09	−4.30	1,352
Obesity	Maine	−6.92	−8.25	−5.60	399
Obesity	Maryland	−5.11	−7.78	−2.45	1,449
Obesity	Massachusetts	−3.57	−5.94	−1.20	1,585
Obesity	Michigan	−5.40	−6.53	−4.27	2,869
Obesity	Minnesota	−5.36	−6.53	−4.18	1,497
Obesity	Mississippi	−4.27	−5.67	−2.86	865
Obesity	Missouri	−4.95	−6.98	−2.93	1,644
Obesity	Montana	−5.67	−8.49	−2.85	315
Obesity	Nebraska	−3.15	−4.74	−1.56	550
Obesity	Nevada	−3.67	−4.87	−2.48	767
Obesity	New Hampshire	−2.80	−4.04	−1.56	347
Obesity	New Jersey	−5.19	−6.17	−4.22	2,154
Obesity	New Mexico	−5.71	−6.82	−4.61	594
Obesity	New York	−5.19	−6.80	−3.59	5,182
Obesity	North Carolina	−4.68	−5.96	−3.40	2,628
Obesity	North Dakota	−2.65	−4.32	−0.98	227
Obesity	Ohio	−5.73	−6.28	−5.18	3,129
Obesity	Oklahoma	−4.94	−6.09	−3.79	1,189
Obesity	Oregon	−3.21	−4.36	−2.07	991
Obesity	Pennsylvania	−4.76	−5.47	−4.05	3,377
Obesity	Rhode Island	−3.64	−5.39	−1.90	246
Obesity	South Carolina	−5.64	−6.83	−4.46	1,301
Obesity	South Dakota	−4.30	−5.52	−3.08	242
Obesity	Tennessee	−5.61	−7.06	−4.16	1,675
Obesity	Texas	−3.87	−4.48	−3.25	6,802
Obesity	Utah	−1.56	−2.61	−0.51	707
Obesity	Vermont	−3.52	−4.60	−2.43	192
Obesity	Virginia	−4.49	−5.82	−3.17	2,152
Obesity	Washington	−5.22	−7.57	−2.88	1,767
Obesity	West Virginia	−3.40	−4.67	−2.14	543
Obesity	Wisconsin	−3.44	−4.32	−2.57	1,523
Obesity	Wyoming	−3.13	−7.58	1.33	159
Diagnosed diabetes	Alabama	−2.33	−3.32	−1.34	1,423
Diagnosed diabetes	Alaska	−2.87	−3.89	−1.84	173
Diagnosed diabetes	Arizona	−2.22	−3.76	−0.69	1,725
Diagnosed diabetes	Arkansas	−2.85	−3.87	−1.84	818
Diagnosed diabetes	California	−1.35	−1.71	−0.98	9,003
Diagnosed diabetes	Colorado	−0.76	−1.28	−0.25	1,423
Diagnosed diabetes	Connecticut	−1.76	−2.78	−0.74	870
Diagnosed diabetes	Delaware	−0.86	−2.34	0.62	256
Diagnosed diabetes	District of Columbia	−0.39	−0.92	0.13	201
Diagnosed diabetes	Florida	−2.46	−3.01	−1.90	5,053
Diagnosed diabetes	Georgia	−2.57	−3.04	−2.09	2,766
Diagnosed diabetes	Hawaii	−1.82	−2.94	−0.70	419
Diagnosed diabetes	Idaho	−1.23	−2.01	−0.46	453
Diagnosed diabetes	Illinois	−1.71	−2.59	−0.83	3,239
Diagnosed diabetes	Indiana	−1.57	−2.43	−0.72	1,678
Diagnosed diabetes	Iowa	−1.28	−2.25	−0.31	890
Diagnosed diabetes	Kansas	−1.75	−2.43	−1.07	811
Diagnosed diabetes	Kentucky	−3.07	−3.50	−2.63	1,287
Diagnosed diabetes	Louisiana	−3.08	−3.65	−2.51	1,352
Diagnosed diabetes	Maine	−2.38	−2.91	−1.86	399
Diagnosed diabetes	Maryland	−1.72	−2.41	−1.03	1,449
Diagnosed diabetes	Massachusetts	−1.43	−2.19	−0.66	1,585
Diagnosed diabetes	Michigan	−2.11	−2.63	−1.60	2,869
Diagnosed diabetes	Minnesota	−1.59	−1.96	−1.22	1,497
Diagnosed diabetes	Mississippi	−3.08	−4.13	−2.04	865
Diagnosed diabetes	Missouri	−2.30	−2.85	−1.76	1,644
Diagnosed diabetes	Montana	−2.87	−3.85	−1.90	315
Diagnosed diabetes	Nebraska	−0.99	−1.47	−0.52	550
Diagnosed diabetes	Nevada	−2.04	−2.93	−1.14	767
Diagnosed diabetes	New Hampshire	−1.35	−1.94	−0.75	347
Diagnosed diabetes	New Jersey	−1.46	−1.94	−0.99	2,154
Diagnosed diabetes	New Mexico	−3.61	−4.53	−2.69	594
Diagnosed diabetes	New York	−2.24	−2.87	−1.61	5,182
Diagnosed diabetes	North Carolina	−2.41	−2.95	−1.87	2,628
Diagnosed diabetes	North Dakota	−1.70	−3.17	−0.23	227
Diagnosed diabetes	Ohio	−2.58	−2.99	−2.18	3,129
Diagnosed diabetes	Oklahoma	−2.95	−3.68	−2.23	1,189
Diagnosed diabetes	Oregon	−1.38	−2.13	−0.62	991
Diagnosed diabetes	Pennsylvania	−1.91	−2.22	−1.59	3,377
Diagnosed diabetes	Rhode Island	−2.01	−2.55	−1.47	246
Diagnosed diabetes	South Carolina	−2.82	−3.47	−2.18	1,301
Diagnosed diabetes	South Dakota	−3.14	−4.61	−1.67	242
Diagnosed diabetes	Tennessee	−2.84	−3.76	−1.91	1,675
Diagnosed diabetes	Texas	−1.86	−2.42	−1.29	6,802
Diagnosed diabetes	Utah	−0.38	−1.20	0.45	707
Diagnosed diabetes	Vermont	−1.09	−1.61	−0.57	192
Diagnosed diabetes	Virginia	−1.49	−1.94	−1.05	2,152
Diagnosed diabetes	Washington	−1.41	−1.93	−0.89	1,767
Diagnosed diabetes	West Virginia	−2.40	−3.84	−0.95	543
Diagnosed diabetes	Wisconsin	−1.03	−1.50	−0.55	1,523
Diagnosed diabetes	Wyoming	−1.02	−2.99	0.94	159
Frequent mental distress	Alabama	−4.47	−4.82	−4.12	1,423
Frequent mental distress	Alaska	−4.04	−5.03	−3.04	173
Frequent mental distress	Arizona	−4.21	−5.30	−3.11	1,725
Frequent mental distress	Arkansas	−4.24	−4.78	−3.70	818
Frequent mental distress	California	−3.34	−3.76	−2.93	9,003
Frequent mental distress	Colorado	−3.44	−4.15	−2.73	1,423
Frequent mental distress	Connecticut	−3.41	−3.88	−2.93	870
Frequent mental distress	Delaware	−3.95	−5.59	−2.31	256
Frequent mental distress	District of Columbia	−3.02	−4.58	−1.45	201
Frequent mental distress	Florida	−3.51	−3.82	−3.20	5,053
Frequent mental distress	Georgia	−4.03	−4.25	−3.80	2,766
Frequent mental distress	Hawaii	−2.41	−2.82	−2.00	419
Frequent mental distress	Idaho	−4.19	−5.20	−3.17	453
Frequent mental distress	Illinois	−3.42	−3.79	−3.05	3,239
Frequent mental distress	Indiana	−4.33	−4.68	−3.98	1,678
Frequent mental distress	Iowa	−4.31	−5.00	−3.62	890
Frequent mental distress	Kansas	−3.96	−4.35	−3.57	811
Frequent mental distress	Kentucky	−4.11	−4.41	−3.80	1,287
Frequent mental distress	Louisiana	−3.71	−4.17	−3.25	1,352
Frequent mental distress	Maine	−3.60	−4.13	−3.07	399
Frequent mental distress	Maryland	−3.93	−4.17	−3.69	1,449
Frequent mental distress	Massachusetts	−3.54	−3.83	−3.26	1,585
Frequent mental distress	Michigan	−4.30	−4.59	−4.00	2,869
Frequent mental distress	Minnesota	−3.95	−4.32	−3.58	1,497
Frequent mental distress	Mississippi	−3.82	−4.30	−3.34	865
Frequent mental distress	Missouri	−4.22	−4.65	−3.79	1,644
Frequent mental distress	Montana	−4.06	−4.83	−3.29	315
Frequent mental distress	Nebraska	−3.52	−3.99	−3.06	550
Frequent mental distress	Nevada	−2.82	−3.21	−2.42	767
Frequent mental distress	New Hampshire	−3.46	−4.09	−2.83	347
Frequent mental distress	New Jersey	−3.18	−3.63	−2.73	2,154
Frequent mental distress	New Mexico	−4.02	−4.70	−3.34	594
Frequent mental distress	New York	−2.93	−3.35	−2.50	5,182
Frequent mental distress	North Carolina	−3.89	−4.14	−3.64	2,628
Frequent mental distress	North Dakota	−3.91	−4.69	−3.13	227
Frequent mental distress	Ohio	−4.71	−4.99	−4.43	3,129
Frequent mental distress	Oklahoma	−4.33	−4.60	−4.06	1,189
Frequent mental distress	Oregon	−4.19	−4.87	−3.50	991
Frequent mental distress	Pennsylvania	−3.60	−3.82	−3.38	3,377
Frequent mental distress	Rhode Island	−2.93	−3.65	−2.22	246
Frequent mental distress	South Carolina	−3.80	−4.32	−3.28	1,301
Frequent mental distress	South Dakota	−4.69	−5.57	−3.81	242
Frequent mental distress	Tennessee	−4.32	−4.50	−4.14	1,675
Frequent mental distress	Texas	−3.83	−4.11	−3.55	6,802
Frequent mental distress	Utah	−3.80	−5.22	−2.38	707
Frequent mental distress	Vermont	−4.37	−5.55	−3.19	192
Frequent mental distress	Virginia	−3.86	−4.08	−3.63	2,152
Frequent mental distress	Washington	−4.30	−4.64	−3.96	1,767
Frequent mental distress	West Virginia	−4.42	−5.08	−3.77	543
Frequent mental distress	Wisconsin	−4.26	−4.76	−3.76	1,523
Frequent mental distress	Wyoming	−3.49	−4.43	−2.55	159
No leisure-time physical activity	Alabama	−8.26	−9.49	−7.02	1,423
No leisure-time physical activity	Alaska	−7.43	−9.28	−5.57	173
No leisure-time physical activity	Arizona	−6.01	−7.81	−4.22	1,725
No leisure-time physical activity	Arkansas	−9.36	−10.76	−7.97	818
No leisure-time physical activity	California	−5.16	−5.83	−4.48	9,003
No leisure-time physical activity	Colorado	−3.81	−4.54	−3.07	1,423
No leisure-time physical activity	Connecticut	−5.60	−6.98	−4.22	870
No leisure-time physical activity	Delaware	−6.14	−7.34	−4.95	256
No leisure-time physical activity	District of Columbia	−3.05	−3.82	−2.27	201
No leisure-time physical activity	Florida	−7.40	−8.10	−6.70	5,053
No leisure-time physical activity	Georgia	−8.61	−9.48	−7.73	2,766
No leisure-time physical activity	Hawaii	−4.83	−7.09	−2.57	419
No leisure-time physical activity	Idaho	−6.49	−7.48	−5.50	453
No leisure-time physical activity	Illinois	−6.54	−7.55	−5.53	3,239
No leisure-time physical activity	Indiana	−7.30	−8.70	−5.91	1,678
No leisure-time physical activity	Iowa	−7.56	−8.65	−6.46	890
No leisure-time physical activity	Kansas	−6.53	−7.42	−5.63	811
No leisure-time physical activity	Kentucky	−9.02	−9.63	−8.41	1,287
No leisure-time physical activity	Louisiana	−8.32	−9.14	−7.50	1,352
No leisure-time physical activity	Maine	−8.03	−9.35	−6.70	399
No leisure-time physical activity	Maryland	−6.69	−7.77	−5.62	1,449
No leisure-time physical activity	Massachusetts	−5.93	−7.56	−4.29	1,585
No leisure-time physical activity	Michigan	−7.22	−8.06	−6.37	2,869
No leisure-time physical activity	Minnesota	−6.25	−6.85	−5.65	1,497
No leisure-time physical activity	Mississippi	−8.38	−10.04	−6.72	865
No leisure-time physical activity	Missouri	−8.13	−8.97	−7.29	1,644
No leisure-time physical activity	Montana	−8.17	−9.75	−6.58	315
No leisure-time physical activity	Nebraska	−5.56	−6.21	−4.91	550
No leisure-time physical activity	Nevada	−5.65	−7.08	−4.22	767
No leisure-time physical activity	New Hampshire	−5.29	−6.12	−4.46	347
No leisure-time physical activity	New Jersey	−5.76	−6.59	−4.92	2,154
No leisure-time physical activity	New Mexico	−7.85	−9.10	−6.59	594
No leisure-time physical activity	New York	−6.95	−8.08	−5.82	5,182
No leisure-time physical activity	North Carolina	−7.53	−8.25	−6.82	2,628
No leisure-time physical activity	North Dakota	−6.37	−8.10	−4.63	227
No leisure-time physical activity	Ohio	−8.44	−8.88	−8.00	3,129
No leisure-time physical activity	Oklahoma	−9.23	−10.46	−7.99	1,189
No leisure-time physical activity	Oregon	−5.06	−5.98	−4.14	991
No leisure-time physical activity	Pennsylvania	−6.93	−7.46	−6.40	3,377
No leisure-time physical activity	Rhode Island	−5.88	−6.26	−5.50	246
No leisure-time physical activity	South Carolina	−8.46	−9.27	−7.64	1,301
No leisure-time physical activity	South Dakota	−7.28	−9.25	−5.30	242
No leisure-time physical activity	Tennessee	−8.52	−9.83	−7.21	1,675
No leisure-time physical activity	Texas	−6.93	−7.78	−6.07	6,802
No leisure-time physical activity	Utah	−3.44	−4.31	−2.56	707
No leisure-time physical activity	Vermont	−5.43	−6.39	−4.47	192
No leisure-time physical activity	Virginia	−5.79	−6.42	−5.16	2,152
No leisure-time physical activity	Washington	−5.31	−6.22	−4.40	1,767
No leisure-time physical activity	West Virginia	−8.03	−9.68	−6.37	543
No leisure-time physical activity	Wisconsin	−6.16	−6.64	−5.68	1,523
No leisure-time physical activity	Wyoming	−4.84	−8.10	−1.57	159
Binge drinking	Alabama	1.26	0.97	1.54	1,423
Binge drinking	Alaska	1.02	0.43	1.61	173
Binge drinking	Arizona	0.91	0.37	1.44	1,725
Binge drinking	Arkansas	1.97	1.23	2.71	818
Binge drinking	California	0.66	−0.07	1.40	9,003
Binge drinking	Colorado	0.65	−0.18	1.47	1,423
Binge drinking	Connecticut	0.15	−0.40	0.70	870
Binge drinking	Delaware	0.95	0.63	1.28	256
Binge drinking	District of Columbia	0.92	0.13	1.70	201
Binge drinking	Florida	1.18	0.86	1.49	5,053
Binge drinking	Georgia	1.35	0.96	1.75	2,766
Binge drinking	Hawaii	0.97	0.41	1.53	419
Binge drinking	Idaho	1.16	0.71	1.61	453
Binge drinking	Illinois	1.54	0.70	2.38	3,239
Binge drinking	Indiana	1.20	0.61	1.79	1,678
Binge drinking	Iowa	1.49	1.23	1.76	890
Binge drinking	Kansas	0.65	0.22	1.07	811
Binge drinking	Kentucky	1.99	1.73	2.24	1,287
Binge drinking	Louisiana	2.33	1.81	2.84	1,352
Binge drinking	Maine	1.74	0.89	2.59	399
Binge drinking	Maryland	−0.77	−1.71	0.17	1,449
Binge drinking	Massachusetts	0.51	−0.26	1.27	1,585
Binge drinking	Michigan	1.12	0.41	1.84	2,869
Binge drinking	Minnesota	0.63	−0.05	1.32	1,497
Binge drinking	Mississippi	1.41	0.90	1.92	865
Binge drinking	Missouri	1.88	1.42	2.34	1,644
Binge drinking	Montana	2.18	1.44	2.93	315
Binge drinking	Nebraska	0.91	0.58	1.23	550
Binge drinking	Nevada	0.89	0.19	1.59	767
Binge drinking	New Hampshire	1.12	0.05	2.20	347
Binge drinking	New Jersey	0.36	−0.19	0.90	2,154
Binge drinking	New Mexico	1.59	1.17	2.02	594
Binge drinking	New York	1.37	0.26	2.48	5,182
Binge drinking	North Carolina	1.57	0.96	2.18	2,628
Binge drinking	North Dakota	1.94	1.42	2.46	227
Binge drinking	Ohio	1.67	1.25	2.09	3,129
Binge drinking	Oklahoma	1.68	0.95	2.41	1,189
Binge drinking	Oregon	1.01	0.59	1.43	991
Binge drinking	Pennsylvania	1.16	0.68	1.63	3,377
Binge drinking	Rhode Island	1.13	0.64	1.62	246
Binge drinking	South Carolina	2.25	1.24	3.26	1,301
Binge drinking	South Dakota	1.32	0.46	2.18	242
Binge drinking	Tennessee	1.99	1.39	2.60	1,675
Binge drinking	Texas	0.89	0.49	1.28	6,802
Binge drinking	Utah	1.44	0.99	1.89	707
Binge drinking	Vermont	1.21	0.28	2.15	192
Binge drinking	Virginia	0.04	−0.72	0.79	2,152
Binge drinking	Washington	0.37	−0.17	0.92	1,767
Binge drinking	West Virginia	1.62	1.11	2.14	543
Binge drinking	Wisconsin	1.43	0.91	1.95	1,523
Binge drinking	Wyoming	0.82	−0.66	2.30	159

Note: Each dot is a regression across the tracts of one state, and every state has at least 60 tracts with all the variables. The intervals use counties as clusters; Delaware and the District of Columbia use HC3 errors because they have too few counties for cluster-robust errors.

Note: PLACES values are modeled from BRFSS responses with census and ACS inputs, and median household income comes from the ACS, so part of each coefficient reflects shared inputs. These are differences between tracts within a state and do not describe what any person does.

Note: The highlighted dot is Maryland, the one state with a negative binge drinking coefficient; its interval includes zero.

Source: CDC PLACES 2024 release (BRFSS 2022) and ACS 2019-2023; modeled estimates, population-weighted (adults in each tract)

Data: vishalsingh.org/zipcode/report/gradients-slopes-and-variance/figures/coefficient_zoo.json

Table 5. The first of nine components holds 51.3% of the variance in nine census measures and the second holds 16.0%

Principal components of poverty, cash assistance, unemployment, education, single-parent households, income, labor-force participation and homeownership across tracts, 2019-2023

Table 5.1. Scree

Two of nine components have an eigenvalue above one, and the first holds 51.3% of the variance

Eigenvalue of each principal component of the correlation matrix of nine standardized tract measures, with the Kaiser line at one

component	conf_high	conf_low	eigenvalue
PC1	4.69	4.54	4.61
PC2	1.48	1.40	1.44
PC3	0.907	0.854	0.880
PC4	0.743	0.699	0.720
PC5	0.543	0.520	0.532
PC6	0.303	0.274	0.286
PC7	0.283	0.263	0.276
PC8	0.186	0.167	0.177
PC9	0.082	0.073	0.077

Table 5.2. First component

The first component gives weight to all nine measures, with a largest absolute loading of 0.41

Loading of each measure on the first principal component, signed so that a positive loading means the measure rises with deprivation, with 95% intervals

conf_high	conf_low	loading	measure
0.391	0.386	0.388	Poverty rate
0.404	0.400	0.402	Households on cash assistance or SNAP
0.262	0.242	0.252	Unemployment rate
0.384	0.372	0.378	Adults with a high school diploma or less
0.310	0.299	0.304	Single-parent households
−0.410	−0.416	−0.412	Median household income (log)
−0.359	−0.370	−0.364	Adults with a bachelor's degree or more
−0.161	−0.192	−0.177	Labor-force participation
−0.218	−0.247	−0.233	Homeownership

Table 5.3. Second component

The second component separates homeownership (−0.59) from labor-force participation (+0.56) and the college share (+0.35)

Loading of each measure on the second principal component, signed so that the college share loads positively, with 95% intervals

conf_high	conf_low	loading	measure
0.169	0.132	0.152	Poverty rate
0.134	0.098	0.116	Households on cash assistance or SNAP
0.211	0.148	0.181	Unemployment rate
−0.285	−0.323	−0.304	Adults with a high school diploma or less
0.264	0.195	0.228	Single-parent households
0.059	0.021	0.040	Median household income (log)
0.372	0.334	0.354	Adults with a bachelor's degree or more
0.584	0.524	0.555	Labor-force participation
−0.573	−0.610	−0.593	Homeownership

Source: ACS 2019-2023, census tracts; principal components of nine standardized measures, unweighted (each tract counted once)

Data: vishalsingh.org/zipcode/report/gradients-slopes-and-variance/figures/nine_measure_pca.json

Table 6. Weighted by population, the same pair of measures has a different correlation at each grain, and homeownership and poverty go from −0.53 across tracts to −0.05 across states

Correlation of six pairs of tract measures across tracts, across counties and across states, weighted by population or counting each unit once, with 95% intervals from a bootstrap over counties or states, 2019-2023

Pair of measures	Tracts, weighted	Counties, weighted	States, weighted	Tracts, each once	Counties, each once	States, each once	States, weighted, lower limit	States, weighted, upper limit	States, each once, lower limit	States, each once, upper limit
College share and income	+0.73	+0.79	+0.83	+0.72	+0.71	+0.81	+0.73	+0.94	+0.73	+0.91
Poverty rate and college share	−0.50	−0.56	−0.69	−0.49	−0.50	−0.52	−0.84	−0.50	−0.81	−0.20
Homeownership and poverty rate	−0.53	−0.40	−0.05	−0.52	−0.36	−0.11	−0.30	+0.17	−0.32	+0.12
Unemployment and poverty rate	+0.43	+0.58	+0.33	+0.45	+0.53	+0.50	+0.08	+0.58	+0.32	+0.65
College share and life expectancy	+0.56	+0.64	+0.70	+0.55	+0.51	+0.48	+0.55	+0.83	+0.13	+0.81
Poverty rate and life expectancy	−0.50	−0.60	−0.66	−0.52	−0.57	−0.78	−0.88	−0.45	−0.89	−0.65

Views: Weighted, tracts to counties (every row); Weighted, counties to states (every row); Each unit once, tracts to counties (every row); Each unit once, counties to states (every row).

Note: Every correlation uses the same 77,775 tracts. County and state values are population-weighted means of the tract values; the weighted views weight each correlation by the population of its unit and the others count each unit once. The county correlations rest on 3,112 counties and the state correlations on 51 states, so the state intervals are wide.

Note: The correlation with life expectancy is between tract life expectancy, a modeled estimate, and census measures.

Note: ACS tract values are survey estimates with sampling error, and the staged file has no margins of error, so they are treated as exact.

Source: ACS 2019-2023 tracts and USALEEP life expectancy, 2010-2015; correlations of tract values and of population-weighted county and state means, population-weighted and unweighted

Data: vishalsingh.org/zipcode/report/gradients-slopes-and-variance/figures/maup_correlations.json

Table 7. 70.3 percent of the variance of tract life expectancy lies between tracts in the same county, 26.7 points of it estimation error, against 34% percent for the ADI

Share of the population-weighted variance of three tract measures between states, between counties in a state and between tracts in a county, percent

level	measure	share
Between states	Tract life expectancy	13.75
Between counties in a state	Tract life expectancy	15.92
Between tracts in a county	Tract life expectancy	70.33
Between states	ADI national percentile	36.64
Between counties in a state	ADI national percentile	29.38
Between tracts in a county	ADI national percentile	33.98
Between states	Nine-measure index percentile	5.13
Between counties in a state	Nine-measure index percentile	23.29
Between tracts in a county	Nine-measure index percentile	71.59

Note: Tract life expectancy is modeled. The mean squared standard error of the tract estimates equals 26.7 percent of the total variance and sits inside the between-tract segment; the 2020-tract standard errors are mapping grade and understate the noise, so the share of the true differences that lies between tracts is lower than the net share in the text.

Note: The life expectancy bar uses 78,008 tracts in 3,112 counties and Connecticut planning regions; the ADI and index bars use the 82,545 tracts of the index. Connecticut's planning regions stand in for its counties.

Source: USALEEP life expectancy, 2010-2015; ACS 2019-2023; Area Deprivation Index, 2023; population-weighted decomposition of the variance, full counts of tracts

Data: vishalsingh.org/zipcode/report/gradients-slopes-and-variance/figures/variance_split.json

Table 8. In each of the 30 most populous counties, tracts at the 10th and 90th percentile of the ADI are between 9 and 69 percentile points apart

Population-weighted mean ADI national percentile of each county (dot) and the 10th to 90th percentile range of its tracts' ADI (whisker), the most populous counties sorted by range, ACS 2019-2023 population

County	Mean ADI	10th percentile of tracts	90th percentile of tracts	Range	Tracts	Residents
Philadelphia, PA	62.4	25.0	93.8	68.8	386	1,578,494
Wayne, MI	72.8	35.3	99.0	63.7	579	1,772,852
Bexar, TX	59.2	26.9	89.0	62.1	369	2,023,627
Dallas, TX	51.8	17.4	78.6	61.1	641	2,597,456
Cook, IL	47.1	17.0	77.0	60.0	1,322	5,158,123
Harris, TX	55.6	24.5	84.3	59.8	1,106	4,739,169
Palm Beach, FL	38.8	12.0	69.7	57.7	363	1,503,596
Tarrant, TX	48.9	22.1	77.6	55.4	445	2,123,556
Broward, FL	41.0	14.2	69.0	54.8	415	1,946,125
Hillsborough, FL	46.0	20.3	71.8	51.6	322	1,476,792
Orange, FL	44.1	20.0	70.4	50.4	262	1,413,087
Maricopa, AZ	36.1	13.0	60.7	47.7	985	4,468,217
Bronx, NY	31.1	15.2	61.0	45.7	340	1,411,947
Miami-Dade, FL	37.2	15.7	61.2	45.5	691	2,663,978
San Bernardino, CA	31.9	11.9	54.2	42.3	461	2,166,515
Clark, NV	39.2	20.0	62.2	42.2	534	2,293,278
Riverside, CA	28.4	11.0	51.3	40.3	514	2,435,341
Sacramento, CA	27.9	11.3	47.4	36.1	361	1,577,120
Queens, NY	16.4	6.0	31.1	25.1	677	2,325,406
Middlesex, MA	14.5	3.7	27.8	24.0	353	1,619,270
Suffolk, NY	19.7	7.8	31.5	23.7	368	1,437,246
King, WA	12.9	3.0	25.5	22.5	491	2,252,223
San Diego, CA	14.0	3.3	25.5	22.2	723	3,203,557
Los Angeles, CA	14.2	3.0	25.0	22.0	2,447	9,781,315
New York, NY	8.9	1.0	20.5	19.5	294	1,614,981
Nassau, NY	13.8	4.7	22.3	17.6	277	1,384,110
Orange, CA	11.2	3.0	20.0	17.0	611	3,164,006
Kings, NY	10.0	2.0	19.0	17.0	773	2,642,467
Alameda, CA	7.8	2.0	18.0	16.0	373	1,649,334
Santa Clara, CA	5.0	1.0	10.0	9.0	407	1,900,063

Note: Tract ADI is a national percentile, so a county whose tracts are all among the least deprived has a narrow range by construction. Counties are chosen by population, not by range.

Note: Connecticut's planning regions stand in for its counties. Puerto Rico is not included.

Source: Area Deprivation Index, 2023; ACS 2019-2023 population; population-weighted percentiles of tract values, full counts of tracts

Data: vishalsingh.org/zipcode/report/gradients-slopes-and-variance/figures/county_adi_spread.json

Table 9. WONDER withholds 71.3% of county-year cells in the lightest birthweight class and 77.6% in the heaviest, against 0.0% in the five middle classes

Share of county-year cells that are withheld, published as zero or published with at least 10 births, by birthweight class, 578 counties named in every year, 2018 to 2024, percent

Birthweight class	Cell status	Share of cells (%)	Cells	Cells in the class	Births in published cells of the class	Largest share of the class's births in withheld cells (%)
499 g or less	Withheld (1 to 9 births)	71.3	2,885	4,046	17,300	60.0
499 g or less	Published zero	8.7	350	4,046	17,300	60.0
499 g or less	Published, 10 or more births	20.0	811	4,046	17,300	60.0
500 to 999 g	Withheld (1 to 9 births)	36.4	1,474	4,046	93,575	12.4
500 to 999 g	Published zero	0.4	16	4,046	93,575	12.4
500 to 999 g	Published, 10 or more births	63.2	2,556	4,046	93,575	12.4
1,000 to 1,499 g	Withheld (1 to 9 births)	18.3	741	4,046	144,348	4.4
1,000 to 1,499 g	Published zero	0.0	0	4,046	144,348	4.4
1,000 to 1,499 g	Published, 10 or more births	81.7	3,305	4,046	144,348	4.4
1,500 to 1,999 g	Withheld (1 to 9 births)	1.1	43	4,046	334,713	0.1
1,500 to 1,999 g	Published zero	0.0	0	4,046	334,713	0.1
1,500 to 1,999 g	Published, 10 or more births	98.9	4,003	4,046	334,713	0.1
2,000 to 2,499 g	Withheld (1 to 9 births)	0.0	0	4,046	1,107,825	0.0
2,000 to 2,499 g	Published zero	0.0	0	4,046	1,107,825	0.0
2,000 to 2,499 g	Published, 10 or more births	100.0	4,046	4,046	1,107,825	0.0
2,500 to 2,999 g	Withheld (1 to 9 births)	0.0	0	4,046	3,979,246	0.0
2,500 to 2,999 g	Published zero	0.0	0	4,046	3,979,246	0.0
2,500 to 2,999 g	Published, 10 or more births	100.0	4,046	4,046	3,979,246	0.0
3,000 to 3,499 g	Withheld (1 to 9 births)	0.0	0	4,046	7,991,060	0.0
3,000 to 3,499 g	Published zero	0.0	0	4,046	7,991,060	0.0
3,000 to 3,499 g	Published, 10 or more births	100.0	4,046	4,046	7,991,060	0.0
3,500 to 3,999 g	Withheld (1 to 9 births)	0.0	0	4,046	5,263,258	0.0
3,500 to 3,999 g	Published zero	0.0	0	4,046	5,263,258	0.0
3,500 to 3,999 g	Published, 10 or more births	100.0	4,046	4,046	5,263,258	0.0
4,000 to 4,499 g	Withheld (1 to 9 births)	0.0	0	4,046	1,289,498	0.0
4,000 to 4,499 g	Published zero	0.0	0	4,046	1,289,498	0.0
4,000 to 4,499 g	Published, 10 or more births	100.0	4,046	4,046	1,289,498	0.0
4,500 to 4,999 g	Withheld (1 to 9 births)	8.3	334	4,046	167,374	1.8
4,500 to 4,999 g	Published zero	0.0	0	4,046	167,374	1.8
4,500 to 4,999 g	Published, 10 or more births	91.7	3,712	4,046	167,374	1.8
5,000 g or more	Withheld (1 to 9 births)	77.6	3,141	4,046	8,863	76.1
5,000 g or more	Published zero	10.9	441	4,046	8,863	76.1
5,000 g or more	Published, 10 or more births	11.5	464	4,046	8,863	76.1
Weight not stated	Withheld (1 to 9 births)	65.0	2,632	4,046	9,365	71.7
Weight not stated	Published zero	27.2	1,100	4,046	9,365	71.7
Weight not stated	Published, 10 or more births	7.8	314	4,046	9,365	71.7

Note: WONDER withholds a cell with fewer than 10 births, so a withheld cell holds at most 9. The counts here are of cells only; nothing is derived for a withheld cell, and the county totals are not used.

Note: The bound in the table is the births in the published cells of a class plus 9 for each withheld cell, which is the largest share of the class's births that the withheld cells could hold.

Note: Cells are for the 578 counties that WONDER names in every year, which have at least 100,000 residents; pooled rows for other counties are not cells of a county.

Source: CDC WONDER natality, county by birthweight class, 2018-2024; full counts of cells

Data: vishalsingh.org/zipcode/report/gradients-slopes-and-variance/figures/wonder_suppression_by_birthweight.json

All estimates

Every fact in facts.json (134), sorted by id. The same values as JSON: facts.json.

Fact id	Value	Unit	Estimand	Population	Period	Interval	n	Weights	Source	Note
adi_state	37%	percent of the variance of the ADI national percentile	share of the population-weighted variance of the ADI national percentile that lies between states (sum-of-squares decomposition)	tracts in the 50 states and DC with all nine measures, an ACS population above zero and an ADI rank	ACS 2019-2023; ADI 2023		82,545 tracts	ACS 2019-2023 tract population	Area Deprivation Index
adi_within	34%	percent of the variance of the ADI national percentile	share of the population-weighted variance of the ADI national percentile that lies between tracts in the same county (sum-of-squares decomposition)	tracts in the 50 states and DC with all nine measures, an ACS population above zero and an ADI rank	ACS 2019-2023; ADI 2023		82,545 tracts	ACS 2019-2023 tract population	Area Deprivation Index
big_le_spread	8.0	years of life expectancy between the 10th and 90th percentile of tract life expectancy	median over counties of the population-weighted 90th minus 10th percentile of tract life expectancy	the 30 most populous counties among those with an ADI rank for at least 20 tracts	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population		30 counties	none (each county counts once)	USALEEP life expectancy	Tract life expectancies are modeled, so part of this spread is estimation error.
big_max	69	ADI percentile points between the 10th and 90th percentile of tract ADI	largest population-weighted 90th minus 10th percentile of tract ADI among the counties	the 30 most populous counties among those with an ADI rank for at least 20 tracts	ACS 2019-2023; ADI 2023		30 counties	none (each county counts once)	Area Deprivation Index
big_min	9	ADI percentile points between the 10th and 90th percentile of tract ADI	smallest population-weighted 90th minus 10th percentile of tract ADI among the counties	the 30 most populous counties among those with an ADI rank for at least 20 tracts	ACS 2019-2023; ADI 2023		30 counties	none (each county counts once)	Area Deprivation Index
big_n	30	counties	number of counties shown (rule)	most populous counties and Connecticut planning regions (ACS 2019-2023)	ACS 2019-2023; ADI 2023			none (definition)	Area Deprivation Index
big_narrow_cut	25	ADI percentile points	a range of this many percentile points or fewer counts as narrow (definition)	the 30 most populous counties among those with an ADI rank for at least 20 tracts	ACS 2019-2023; ADI 2023			none (definition)	Area Deprivation Index
big_narrow_n	11	counties	count of the counties whose 90th minus 10th percentile of tract ADI is at most the narrow cut	the 30 most populous counties among those with an ADI rank for at least 20 tracts	ACS 2019-2023; ADI 2023		30 counties	none (full count)	Area Deprivation Index
binge_median	+1.2	points of prevalence per doubling of tract median household income	median over states of the state coefficient: a population-weighted regression across the state's tracts of tract prevalence on the log of median household income, with tract shares of residents ages 65 and over, Black alone and Hispanic as controls, times the natural log of 2	adults: binge drinking, states and the District of Columbia with at least 60 tracts that have income, the three controls and the measure	BRFSS 2022 (PLACES 2024 release); ACS 2019-2023		51 states	adult population of the tract (PLACES)	CDC PLACES	A median of the state estimates.
binge_pos_n	50	states	count of states where the binge drinking coefficient is above zero	states and the District of Columbia with at least 60 tracts that have income, the three controls and the measure	BRFSS 2022 (PLACES 2024 release); ACS 2019-2023		51 states	none (full count)	CDC PLACES
bw_heavy_min	5,000	grams	lower limit of the heaviest class (definition)	births in the heaviest birthweight class of the WONDER file	2018 to 2024			none (definition)	CDC WONDER natality
bw_light_max	499	grams	upper limit of the lightest class (definition)	births in the lightest birthweight class of the WONDER file	2018 to 2024			none (definition)	CDC WONDER natality
idx_within	72%	percent of the variance of the nine-measure index percentile	share of the population-weighted variance of the first-component index percentile that lies between tracts in the same county (sum-of-squares decomposition)	tracts in the 50 states and DC with all nine measures, an ACS population above zero and an ADI rank	ACS 2019-2023; ADI 2023		82,545 tracts	ACS 2019-2023 tract population	American Community Survey
ladder_county	1.12	years of life expectancy at birth lower per ten-point step in the ADI national percentile	population-weighted least-squares slope of tract life expectancy on the ADI national percentile, times ten, sign reversed, with a constant for each county; cluster-robust interval (t on the number of counties minus one)	residents of non-institutional tracts in the 50 states and DC with a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population	95% CI 1.07 to 1.16	77,722 tracts	ACS 2019-2023 tract population	USALEEP life expectancy
ladder_ratio	1.3	ratio of slopes	within-county slope over the slope across all tracts	residents of non-institutional tracts in the 50 states and DC with a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population		77,936 tracts	ACS 2019-2023 tract population	USALEEP life expectancy	A ratio of two point estimates.
ladder_raw	0.84	years of life expectancy at birth lower per ten-point step in the ADI national percentile	population-weighted least-squares slope of tract life expectancy on the ADI national percentile, times ten, sign reversed; cluster-robust interval (t on the number of states minus one)	residents of non-institutional tracts in the 50 states and DC with a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population	95% CI 0.75 to 0.92	77,936 tracts	ACS 2019-2023 tract population	USALEEP life expectancy
ladder_state	0.92	years of life expectancy at birth lower per ten-point step in the ADI national percentile	population-weighted least-squares slope of tract life expectancy on the ADI national percentile, times ten, sign reversed, with a constant for each state; cluster-robust interval (t on the number of states minus one)	residents of non-institutional tracts in the 50 states and DC with a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population	95% CI 0.78 to 1.05	77,936 tracts	ACS 2019-2023 tract population	USALEEP life expectancy
ladder_unw_maxdiff	0.015	years of life expectancy per ten-point step	largest absolute difference, over the three specifications, between the population-weighted slope and the slope that counts each tract once	residents of non-institutional tracts in the 50 states and DC with a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population		77,936 tracts	ACS 2019-2023 tract population	USALEEP life expectancy	A difference of point estimates.
map_floor	20	pooled deaths	minimum number of pooled deaths for a county to be mapped (rule)	counties on a map of WONDER deaths	2018 to 2024			none (definition)	CDC WONDER underlying cause of death
maup_cle_state	+0.71	correlation coefficient	population-weighted correlation of the college share and life expectancy across states (values are population-weighted means of tract values); bootstrap interval over states	tracts in the 50 states and DC with a life expectancy estimate and all five ACS measures of the correlations	ACS 2019-2023; deaths in 2010 to 2015 (USALEEP)	95% CI 0.55 to 0.83	51 states	population of the unit (ACS 2019-2023)	American Community Survey
maup_cle_state_u	+0.48	correlation coefficient	unweighted (each unit counted once) correlation of the college share and life expectancy across states (values are population-weighted means of tract values); bootstrap interval over states	tracts in the 50 states and DC with a life expectancy estimate and all five ACS measures of the correlations	ACS 2019-2023; deaths in 2010 to 2015 (USALEEP)	95% CI 0.13 to 0.81	51 states	none (each unit counts once)	American Community Survey
maup_cle_tract	+0.56	correlation coefficient	population-weighted correlation of the college share and life expectancy across tracts (values are population-weighted means of tract values); bootstrap interval over counties	tracts in the 50 states and DC with a life expectancy estimate and all five ACS measures of the correlations	ACS 2019-2023; deaths in 2010 to 2015 (USALEEP)	95% CI 0.54 to 0.58	77,775 tracts	population of the unit (ACS 2019-2023)	American Community Survey
maup_cle_tract_u	+0.55	correlation coefficient	unweighted (each unit counted once) correlation of the college share and life expectancy across tracts (values are population-weighted means of tract values); bootstrap interval over counties	tracts in the 50 states and DC with a life expectancy estimate and all five ACS measures of the correlations	ACS 2019-2023; deaths in 2010 to 2015 (USALEEP)	95% CI 0.53 to 0.57	77,775 tracts	none (each unit counts once)	American Community Survey
maup_coll_state	+0.83	correlation coefficient	population-weighted correlation of the college share and the log of median household income across states (values are population-weighted means of tract values); bootstrap interval over states	tracts in the 50 states and DC with a life expectancy estimate and all five ACS measures of the correlations	ACS 2019-2023; deaths in 2010 to 2015 (USALEEP)	95% CI 0.73 to 0.94	51 states	population of the unit (ACS 2019-2023)	American Community Survey
maup_coll_tract	+0.73	correlation coefficient	population-weighted correlation of the college share and the log of median household income across tracts (values are population-weighted means of tract values); bootstrap interval over counties	tracts in the 50 states and DC with a life expectancy estimate and all five ACS measures of the correlations	ACS 2019-2023; deaths in 2010 to 2015 (USALEEP)	95% CI 0.71 to 0.74	77,775 tracts	population of the unit (ACS 2019-2023)	American Community Survey
maup_n_counties	3,112	counties and Connecticut planning regions	count of counties in the correlations	tracts in the 50 states and DC with a life expectancy estimate and all five ACS measures of the correlations	ACS 2019-2023; deaths in 2010 to 2015 (USALEEP)			none (full count)	American Community Survey
maup_n_flip	2	pairs of measures	count of pairs for which the correlation is larger across states than across tracts under one weighting and not under the other	tracts in the 50 states and DC with a life expectancy estimate and all five ACS measures of the correlations	ACS 2019-2023; deaths in 2010 to 2015 (USALEEP)		6 pairs	none (full count)	American Community Survey
maup_n_larger	4	pairs of measures	count of pairs whose correlation, weighted by population, is larger in absolute value across states than across tracts	tracts in the 50 states and DC with a life expectancy estimate and all five ACS measures of the correlations	ACS 2019-2023; deaths in 2010 to 2015 (USALEEP)		6 pairs	none (full count)	American Community Survey
maup_n_larger_u	4	pairs of measures	count of pairs whose correlation, counting each unit once, is larger in absolute value across states than across tracts	tracts in the 50 states and DC with a life expectancy estimate and all five ACS measures of the correlations	ACS 2019-2023; deaths in 2010 to 2015 (USALEEP)		6 pairs	none (full count)	American Community Survey
maup_n_pairs	6	pairs of measures	count of pairs (rule)	tracts in the 50 states and DC with a life expectancy estimate and all five ACS measures of the correlations	ACS 2019-2023; deaths in 2010 to 2015 (USALEEP)			none (definition)	American Community Survey
maup_n_states	51	states	count of states in the correlations	tracts in the 50 states and DC with a life expectancy estimate and all five ACS measures of the correlations	ACS 2019-2023; deaths in 2010 to 2015 (USALEEP)			none (full count)	American Community Survey
maup_n_tracts	77,775	tracts	count of tracts in the correlations	tracts in the 50 states and DC with a life expectancy estimate and all five ACS measures of the correlations	ACS 2019-2023; deaths in 2010 to 2015 (USALEEP)			none (full count)	American Community Survey
maup_own_county	−0.40	correlation coefficient	population-weighted correlation of homeownership and the poverty rate across counties (values are population-weighted means of tract values); bootstrap interval over states	tracts in the 50 states and DC with a life expectancy estimate and all five ACS measures of the correlations	ACS 2019-2023; deaths in 2010 to 2015 (USALEEP)	95% CI −0.53 to −0.26	3,112 counties	population of the unit (ACS 2019-2023)	American Community Survey
maup_own_state	−0.05	correlation coefficient	population-weighted correlation of homeownership and the poverty rate across states (values are population-weighted means of tract values); bootstrap interval over states	tracts in the 50 states and DC with a life expectancy estimate and all five ACS measures of the correlations	ACS 2019-2023; deaths in 2010 to 2015 (USALEEP)	95% CI −0.30 to 0.17	51 states	population of the unit (ACS 2019-2023)	American Community Survey
maup_own_state_u	−0.11	correlation coefficient	unweighted (each unit counted once) correlation of homeownership and the poverty rate across states (values are population-weighted means of tract values); bootstrap interval over states	tracts in the 50 states and DC with a life expectancy estimate and all five ACS measures of the correlations	ACS 2019-2023; deaths in 2010 to 2015 (USALEEP)	95% CI −0.32 to 0.11	51 states	none (each unit counts once)	American Community Survey
maup_own_tract	−0.53	correlation coefficient	population-weighted correlation of homeownership and the poverty rate across tracts (values are population-weighted means of tract values); bootstrap interval over counties	tracts in the 50 states and DC with a life expectancy estimate and all five ACS measures of the correlations	ACS 2019-2023; deaths in 2010 to 2015 (USALEEP)	95% CI −0.55 to −0.51	77,775 tracts	population of the unit (ACS 2019-2023)	American Community Survey
maup_own_tract_u	−0.52	correlation coefficient	unweighted (each unit counted once) correlation of homeownership and the poverty rate across tracts (values are population-weighted means of tract values); bootstrap interval over counties	tracts in the 50 states and DC with a life expectancy estimate and all five ACS measures of the correlations	ACS 2019-2023; deaths in 2010 to 2015 (USALEEP)	95% CI −0.54 to −0.50	77,775 tracts	none (each unit counts once)	American Community Survey
maup_unemp_county	+0.59	correlation coefficient	population-weighted correlation of the unemployment rate and the poverty rate across counties (values are population-weighted means of tract values); bootstrap interval over states	tracts in the 50 states and DC with a life expectancy estimate and all five ACS measures of the correlations	ACS 2019-2023; deaths in 2010 to 2015 (USALEEP)	95% CI 0.52 to 0.66	3,112 counties	population of the unit (ACS 2019-2023)	American Community Survey
maup_unemp_county_u	+0.53	correlation coefficient	unweighted (each unit counted once) correlation of the unemployment rate and the poverty rate across counties (values are population-weighted means of tract values); bootstrap interval over states	tracts in the 50 states and DC with a life expectancy estimate and all five ACS measures of the correlations	ACS 2019-2023; deaths in 2010 to 2015 (USALEEP)	95% CI 0.44 to 0.61	3,112 counties	none (each unit counts once)	American Community Survey
maup_unemp_state	+0.33	correlation coefficient	population-weighted correlation of the unemployment rate and the poverty rate across states (values are population-weighted means of tract values); bootstrap interval over states	tracts in the 50 states and DC with a life expectancy estimate and all five ACS measures of the correlations	ACS 2019-2023; deaths in 2010 to 2015 (USALEEP)	95% CI 0.08 to 0.59	51 states	population of the unit (ACS 2019-2023)	American Community Survey
maup_unemp_state_u	+0.50	correlation coefficient	unweighted (each unit counted once) correlation of the unemployment rate and the poverty rate across states (values are population-weighted means of tract values); bootstrap interval over states	tracts in the 50 states and DC with a life expectancy estimate and all five ACS measures of the correlations	ACS 2019-2023; deaths in 2010 to 2015 (USALEEP)	95% CI 0.32 to 0.65	51 states	none (each unit counts once)	American Community Survey
maup_unemp_tract	+0.43	correlation coefficient	population-weighted correlation of the unemployment rate and the poverty rate across tracts (values are population-weighted means of tract values); bootstrap interval over counties	tracts in the 50 states and DC with a life expectancy estimate and all five ACS measures of the correlations	ACS 2019-2023; deaths in 2010 to 2015 (USALEEP)	95% CI 0.41 to 0.46	77,775 tracts	population of the unit (ACS 2019-2023)	American Community Survey
maup_unemp_tract_u	+0.45	correlation coefficient	unweighted (each unit counted once) correlation of the unemployment rate and the poverty rate across tracts (values are population-weighted means of tract values); bootstrap interval over counties	tracts in the 50 states and DC with a life expectancy estimate and all five ACS measures of the correlations	ACS 2019-2023; deaths in 2010 to 2015 (USALEEP)	95% CI 0.42 to 0.47	77,775 tracts	none (each unit counts once)	American Community Survey
metro_cluster_min	5	counties	minimum number of counties in a metro for cluster-robust errors on counties (rule); a metro with fewer uses HC3 errors	residents of metros (core-based statistical areas) with at least 40 tracts that have a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population			none (definition)	USALEEP life expectancy
metro_flat_edge	0.70	years of life expectancy at birth lower per ten-point step in the ADI national percentile	5th percentile over metros of the estimated metro slope, sign reversed	residents of metros (core-based statistical areas) with at least 40 tracts that have a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population		267 metros	none (each metro counts once)	USALEEP life expectancy	A percentile of estimates, which are stretched by estimation error.
metro_flat_edge_sh	0.88	years of life expectancy at birth lower per ten-point step in the ADI national percentile	5th percentile over metros of the metro slope shrunk toward the mean by tau2 over tau2 plus its squared standard error, sign reversed	residents of metros (core-based statistical areas) with at least 40 tracts that have a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population		267 metros	none (each metro counts once)	USALEEP life expectancy
metro_floor	40	tracts	minimum number of tracts with a life expectancy estimate and an ADI rank for a metro to enter (rule)	metros	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population			none (definition)	USALEEP life expectancy
metro_floor_high	100	tracts	highest minimum number of tracts tried (rule)	metros	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population			none (definition)	USALEEP life expectancy
metro_floor_low	25	tracts	lowest minimum number of tracts tried (rule)	metros	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population			none (definition)	USALEEP life expectancy
metro_median	1.20	years of life expectancy at birth lower per ten-point step in the ADI national percentile	median over metros of the metro slope, sign reversed; bootstrap interval over metros	residents of metros (core-based statistical areas) with at least 40 tracts that have a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population	95% CI 1.14 to 1.23	267 metros	none (each metro counts once)	USALEEP life expectancy
metro_n_clustered	64	metros	count of metros with at least five counties, whose errors are clustered on counties (the others use HC3 errors)	residents of metros (core-based statistical areas) with at least 40 tracts that have a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population			none (full count)	USALEEP life expectancy
metro_n_lower	267	metros	count of metros with life expectancy lower in more deprived tracts: the reported slope (years lower per ten-point step) is above zero	residents of metros (core-based statistical areas) with at least 40 tracts that have a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population		267 metros	none (full count)	USALEEP life expectancy
metro_n_sig	260	metros	count of metros whose 95% interval for the reported slope lies above zero	residents of metros (core-based statistical areas) with at least 40 tracts that have a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population		267 metros	none (full count)	USALEEP life expectancy
metro_noise	48%	percent of the variance of the estimated metro slopes	mean squared standard error of the metro slopes over the variance of the estimated slopes across metros	residents of metros (core-based statistical areas) with at least 40 tracts that have a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population		267 metros	none (each metro counts once)	USALEEP life expectancy	The standard errors treat tracts as independent in metros with fewer than five counties, so this share may be too small.
metro_pop_share	82%	percent of residents	share of residents of the tract sample who live in the metros of the analysis	residents of non-institutional tracts in the 50 states and DC with a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population		267 metros	ACS 2019-2023 tract population	USALEEP life expectancy
metro_ratio	2.4	ratio of slopes	95th over 5th percentile of the estimated metro slopes, sign reversed	residents of metros (core-based statistical areas) with at least 40 tracts that have a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population		267 metros	none (each metro counts once)	USALEEP life expectancy	A ratio of two percentiles.
metro_sd	0.31	years of life expectancy at birth lower per ten-point step in the ADI national percentile	standard deviation across metros of the estimated metro slope	residents of metros (core-based statistical areas) with at least 40 tracts that have a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population		267 metros	none (each metro counts once)	USALEEP life expectancy
metro_steep_edge	1.67	years of life expectancy at birth lower per ten-point step in the ADI national percentile	95th percentile over metros of the estimated metro slope, sign reversed	residents of metros (core-based statistical areas) with at least 40 tracts that have a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population		267 metros	none (each metro counts once)	USALEEP life expectancy	A percentile of estimates, which are stretched by estimation error.
metro_steep_edge_sh	1.46	years of life expectancy at birth lower per ten-point step in the ADI national percentile	95th percentile over metros of the metro slope shrunk toward the mean by tau2 over tau2 plus its squared standard error, sign reversed	residents of metros (core-based statistical areas) with at least 40 tracts that have a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population		267 metros	none (each metro counts once)	USALEEP life expectancy
metro_tau	0.22	years of life expectancy at birth lower per ten-point step in the ADI national percentile	estimated standard deviation across metros of the true metro slope: the square root of the variance of the estimates minus the mean squared standard error	residents of metros (core-based statistical areas) with at least 40 tracts that have a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population		267 metros	none (each metro counts once)	USALEEP life expectancy
n_ladder_county_tracts	77,722	tracts	count of tracts in the county fixed-effects regression	residents of non-institutional tracts in the 50 states and DC with a life expectancy estimate and an ADI rank, in counties with at least two such tracts	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population			none (full count)	USALEEP life expectancy	A tract that is alone in its county has no within-county contrast and leaves the fit.
n_ladder_tracts	77,936	tracts	count of tracts in the regressions	residents of non-institutional tracts in the 50 states and DC with a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population			none (full count)	USALEEP life expectancy
n_metros	267	metros	count of metros	residents of metros (core-based statistical areas) with at least 40 tracts that have a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population			none (full count)	USALEEP life expectancy	Metropolitan and micropolitan statistical areas; each tract takes the area of its county (Census county file).
n_metros_100	134	metros	count of metros at this minimum number of tracts	core-based statistical areas with at least 100 tracts that have a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population			none (full count)	USALEEP life expectancy
n_metros_25	403	metros	count of metros at this minimum number of tracts	core-based statistical areas with at least 25 tracts that have a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population			none (full count)	USALEEP life expectancy
pca1_max	0.41	absolute loading on the first component	largest absolute loading of a measure on the first component	tracts in the 50 states and DC with an ACS population above zero, all nine measures and an ADI rank	ACS 2019-2023; ADI 2023		82,545 tracts	none (each tract counts once)	American Community Survey
pca2_col	+0.35	loading on the second component	loading of adults with a bachelor's degree or more on the second component, oriented so that the college share loads positively	tracts in the 50 states and DC with an ACS population above zero, all nine measures and an ADI rank	ACS 2019-2023; ADI 2023		82,545 tracts	none (each tract counts once)	American Community Survey
pca2_lfp	+0.56	loading on the second component	loading of labor-force participation on the second component, oriented so that the college share loads positively	tracts in the 50 states and DC with an ACS population above zero, all nine measures and an ADI rank	ACS 2019-2023; ADI 2023		82,545 tracts	none (each tract counts once)	American Community Survey
pca2_own	−0.59	loading on the second component	loading of homeownership on the second component, oriented so that the college share loads positively	tracts in the 50 states and DC with an ACS population above zero, all nine measures and an ADI rank	ACS 2019-2023; ADI 2023		82,545 tracts	none (each tract counts once)	American Community Survey
pca_alpha	0.87	standardized Cronbach's alpha	alpha from the mean correlation between the nine measures after each is signed so that higher means more deprived; bootstrap interval over counties	tracts in the 50 states and DC with an ACS population above zero, all nine measures and an ADI rank	ACS 2019-2023; ADI 2023	95% CI 0.86 to 0.87	82,545 tracts	none (each tract counts once)	American Community Survey
pca_cum12	67.2%	percent of the variance of the nine standardized measures	sum of the first two eigenvalues of the correlation matrix of the nine standardized measures over nine (each tract counted once); bootstrap interval over counties	tracts in the 50 states and DC with an ACS population above zero, all nine measures and an ADI rank	ACS 2019-2023; ADI 2023	95% CI 66.5% to 68.0%	82,545 tracts	none (each tract counts once)	American Community Survey
pca_dims	2	components	count of components with an eigenvalue above one (the Kaiser rule)	tracts in the 50 states and DC with an ACS population above zero, all nine measures and an ADI rank	ACS 2019-2023; ADI 2023		82,545 tracts	none (full count)	American Community Survey
pca_evr1	51.3%	percent of the variance of the nine standardized measures	first eigenvalue of the correlation matrix of the nine standardized measures over nine (each tract counted once); bootstrap interval over counties	tracts in the 50 states and DC with an ACS population above zero, all nine measures and an ADI rank	ACS 2019-2023; ADI 2023	95% CI 50.4% to 52.1%	82,545 tracts	none (each tract counts once)	American Community Survey
pca_evr1_w	51.4%	percent of the variance of the nine standardized measures	first eigenvalue over nine, from the correlation matrix weighted by ACS tract population	tracts in the 50 states and DC with an ACS population above zero, all nine measures and an ADI rank	ACS 2019-2023; ADI 2023		82,545 tracts	ACS 2019-2023 tract population	American Community Survey
pca_evr2	16.0%	percent of the variance of the nine standardized measures	second eigenvalue of the correlation matrix of the nine standardized measures over nine (each tract counted once); bootstrap interval over counties	tracts in the 50 states and DC with an ACS population above zero, all nine measures and an ADI rank	ACS 2019-2023; ADI 2023	95% CI 15.5% to 16.4%	82,545 tracts	none (each tract counts once)	American Community Survey
pca_jack_max	2.3	percentile points	largest, over the nine measures, of the median absolute change in a tract's index percentile when that measure is dropped and the first component is refitted	tracts in the 50 states and DC with an ACS population above zero, all nine measures and an ADI rank	ACS 2019-2023; ADI 2023		82,545 tracts	none (each tract counts once)	American Community Survey
pca_n	82,545	tracts	count of tracts in the index	tracts in the 50 states and DC with an ACS population above zero, all nine measures and an ADI rank	ACS 2019-2023; ADI 2023			none (full count)	American Community Survey
pca_n_dropped	1,066	tracts	count of such tracts that lack a measure or an ADI rank	tracts in the 50 states and DC with an ACS population above zero	ACS 2019-2023; ADI 2023			none (full count)	American Community Survey
pca_r_collhs	−0.90	correlation coefficient	correlation across tracts of the share of adults with a bachelor's degree or more with the share with a high school diploma or less, each tract counted once; bootstrap interval over counties	tracts in the 50 states and DC with an ACS population above zero, all nine measures and an ADI rank	ACS 2019-2023; ADI 2023	95% CI −0.91 to −0.90	82,545 tracts	none (each tract counts once)	American Community Survey
pca_rho_adi	+0.68	Spearman rank correlation	rank correlation across tracts of the first-component index with the ADI national percentile, each tract counted once; bootstrap interval over counties holding the ranks fixed	tracts in the 50 states and DC with an ACS population above zero, all nine measures and an ADI rank	ACS 2019-2023; ADI 2023	95% CI 0.61 to 0.74	82,545 tracts	none (each tract counts once)	American Community Survey
pca_rho_avg	+0.997	Spearman rank correlation	rank correlation across tracts of the first-component index with the equal-weight average of the nine signed standardized measures, each tract counted once; bootstrap interval over counties holding the ranks fixed	tracts in the 50 states and DC with an ACS population above zero, all nine measures and an ADI rank	ACS 2019-2023; ADI 2023	95% CI 0.996 to 0.997	82,545 tracts	none (each tract counts once)	American Community Survey
pca_rho_fa	+0.994	Spearman rank correlation	rank correlation across tracts of the first-component index with the score of a one-factor factor analysis of the nine measures, each tract counted once; bootstrap interval over counties holding the ranks fixed	tracts in the 50 states and DC with an ACS population above zero, all nine measures and an ADI rank	ACS 2019-2023; ADI 2023	95% CI 0.993 to 0.994	82,545 tracts	none (each tract counts once)	American Community Survey
r_disp_u	−0.14	correlation coefficient	correlation across metros of the metro slope (years lower per ten-point step) with the dispersion of tract ADI in the metro (population-weighted standard deviation), each metro counted once; bootstrap interval over metros	residents of metros (core-based statistical areas) with at least 40 tracts that have a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population	95% CI −0.27 to 0.01	267 metros	none (each metro counts once)	USALEEP life expectancy
r_disp_w	−0.21	correlation coefficient	correlation across metros of the metro slope (years lower per ten-point step) with the dispersion of tract ADI in the metro (population-weighted standard deviation), weighted by metro population; bootstrap interval over metros	residents of metros (core-based statistical areas) with at least 40 tracts that have a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population	95% CI −0.46 to 0.12	267 metros	ACS 2019-2023 tract population summed to the metro	USALEEP life expectancy
r_income_u	+0.05	correlation coefficient	correlation across metros of the metro slope (years lower per ten-point step) with the log of the metro median household income (the population-weighted median of its tracts' median incomes), each metro counted once; bootstrap interval over metros	residents of metros (core-based statistical areas) with at least 40 tracts that have a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population	95% CI −0.11 to 0.19	267 metros	none (each metro counts once)	USALEEP life expectancy
r_income_w	+0.13	correlation coefficient	correlation across metros of the metro slope (years lower per ten-point step) with the log of the metro median household income (the population-weighted median of its tracts' median incomes), weighted by metro population; bootstrap interval over metros	residents of metros (core-based statistical areas) with at least 40 tracts that have a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population	95% CI −0.24 to 0.46	267 metros	ACS 2019-2023 tract population summed to the metro	USALEEP life expectancy
r_size_u	−0.06	correlation coefficient	correlation across metros of the metro slope (years lower per ten-point step) with the log of the metro population, each metro counted once; bootstrap interval over metros	residents of metros (core-based statistical areas) with at least 40 tracts that have a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population	95% CI −0.18 to 0.08	267 metros	none (each metro counts once)	USALEEP life expectancy
r_size_w	−0.20	correlation coefficient	correlation across metros of the metro slope (years lower per ten-point step) with the log of the metro population, weighted by metro population; bootstrap interval over metros	residents of metros (core-based statistical areas) with at least 40 tracts that have a life expectancy estimate and an ADI rank	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts); ADI 2023; ACS 2019-2023 population	95% CI −0.47 to 0.10	267 metros	ACS 2019-2023 tract population summed to the metro	USALEEP life expectancy
range_floor	20	tracts	minimum number of tracts with an ADI rank for a county to enter (rule)	counties and Connecticut planning regions with at least 20 tracts that have an ADI rank	ACS 2019-2023; ADI 2023			none (definition)	Area Deprivation Index
range_floor_high	40	tracts	a minimum number of tracts with an ADI rank tried in the sweep (rule)	counties and Connecticut planning regions with at least 20 tracts that have an ADI rank	ACS 2019-2023; ADI 2023			none (definition)	Area Deprivation Index
range_floor_low	10	tracts	a minimum number of tracts with an ADI rank tried in the sweep (rule)	counties and Connecticut planning regions with at least 20 tracts that have an ADI rank	ACS 2019-2023; ADI 2023			none (definition)	Area Deprivation Index
range_median	39	ADI percentile points between the 10th and 90th percentile of tract ADI	median over counties of the population-weighted 90th minus 10th percentile of tract ADI	counties and Connecticut planning regions with at least 20 tracts that have an ADI rank	ACS 2019-2023; ADI 2023		757 counties	none (each county counts once)	Area Deprivation Index
range_median_10	34	ADI percentile points between the 10th and 90th percentile of tract ADI	median over counties of the population-weighted 90th minus 10th percentile of tract ADI	counties and Connecticut planning regions with at least 10 tracts that have an ADI rank	ACS 2019-2023; ADI 2023		1,319 counties	none (each county counts once)	Area Deprivation Index
range_median_40	43	ADI percentile points between the 10th and 90th percentile of tract ADI	median over counties of the population-weighted 90th minus 10th percentile of tract ADI	counties and Connecticut planning regions with at least 40 tracts that have an ADI rank	ACS 2019-2023; ADI 2023		421 counties	none (each county counts once)	Area Deprivation Index
range_n_counties	757	counties and Connecticut planning regions	count of counties	counties and Connecticut planning regions with at least 20 tracts that have an ADI rank	ACS 2019-2023; ADI 2023			none (full count)	Area Deprivation Index
range_n_counties_10	1,319	counties and Connecticut planning regions	count of counties	counties and Connecticut planning regions with at least 10 tracts that have an ADI rank	ACS 2019-2023; ADI 2023			none (full count)	Area Deprivation Index
range_n_counties_40	421	counties and Connecticut planning regions	count of counties	counties and Connecticut planning regions with at least 40 tracts that have an ADI rank	ACS 2019-2023; ADI 2023			none (full count)	Area Deprivation Index
range_r_unw	+0.27	correlation coefficient	correlation across counties of the population-weighted mean tract ADI with the 90th minus 10th percentile of tract ADI, each county counted once; bootstrap interval over states	counties and Connecticut planning regions with at least 20 tracts that have an ADI rank	ACS 2019-2023; ADI 2023	95% CI 0.06 to 0.44	757 counties	none (each county counts once)	Area Deprivation Index
range_r_unw_10	−0.08	correlation coefficient	correlation across counties of the population-weighted mean tract ADI with the 90th minus 10th percentile of tract ADI, each county counted once; bootstrap interval over states	counties and Connecticut planning regions with at least 10 tracts that have an ADI rank	ACS 2019-2023; ADI 2023	95% CI −0.25 to 0.08	1,319 counties	none (each county counts once)	Area Deprivation Index
range_r_unw_40	+0.62	correlation coefficient	correlation across counties of the population-weighted mean tract ADI with the 90th minus 10th percentile of tract ADI, each county counted once; bootstrap interval over states	counties and Connecticut planning regions with at least 40 tracts that have an ADI rank	ACS 2019-2023; ADI 2023	95% CI 0.45 to 0.74	421 counties	none (each county counts once)	Area Deprivation Index
range_r_w	+0.63	correlation coefficient	correlation across counties of the population-weighted mean tract ADI with the 90th minus 10th percentile of tract ADI, weighted by county population; bootstrap interval over states	counties and Connecticut planning regions with at least 20 tracts that have an ADI rank	ACS 2019-2023; ADI 2023	95% CI 0.37 to 0.75	757 counties	ACS 2019-2023 county population	Area Deprivation Index
range_r_w_10	+0.47	correlation coefficient	correlation across counties of the population-weighted mean tract ADI with the 90th minus 10th percentile of tract ADI, weighted by county population; bootstrap interval over states	counties and Connecticut planning regions with at least 10 tracts that have an ADI rank	ACS 2019-2023; ADI 2023	95% CI 0.16 to 0.63	1,319 counties	ACS 2019-2023 county population	Area Deprivation Index
range_r_w_40	+0.77	correlation coefficient	correlation across counties of the population-weighted mean tract ADI with the 90th minus 10th percentile of tract ADI, weighted by county population; bootstrap interval over states	counties and Connecticut planning regions with at least 40 tracts that have an ADI rank	ACS 2019-2023; ADI 2023	95% CI 0.58 to 0.85	421 counties	ACS 2019-2023 county population	Area Deprivation Index
smoke_median	−4.7	points of prevalence per doubling of tract median household income	median over states of the state coefficient: a population-weighted regression across the state's tracts of tract prevalence on the log of median household income, with tract shares of residents ages 65 and over, Black alone and Hispanic as controls, times the natural log of 2	adults: current cigarette smoking, states and the District of Columbia with at least 60 tracts that have income, the three controls and the measure	BRFSS 2022 (PLACES 2024 release); ACS 2019-2023		51 states	adult population of the tract (PLACES)	CDC PLACES	A median of the state estimates.
supp_cell_max	9	births	most births a withheld cell can hold (the published threshold minus one)	withheld county-year cells	2018 to 2024			none (definition)	CDC WONDER natality
supp_floor	10	births	CDC WONDER withholds a cell with fewer births than this (rule)	county-year cells	2018 to 2024			none (definition)	CDC WONDER natality
supp_heavy	77.6%	percent of cells	share of the cells of this class that WONDER withholds	county-year cells of the 578 counties that WONDER names in every year in the heaviest class	2018 to 2024		4,046 cells	none (full count)	CDC WONDER natality
supp_light	71.3%	percent of cells	share of the cells of this class that WONDER withholds	county-year cells of the 578 counties that WONDER names in every year in the lightest class	2018 to 2024		4,046 cells	none (full count)	CDC WONDER natality
supp_max_heavy	76%	percent of the births in the class	largest share of the class's births the withheld cells could hold: 9 births in each withheld cell, over the births in published cells plus those	county-year cells of the 578 counties that WONDER names in every year in the heaviest class	2018 to 2024		3,141 cells	none (an upper limit from the cell count)	CDC WONDER natality
supp_max_light	60%	percent of the births in the class	largest share of the class's births the withheld cells could hold: 9 births in each withheld cell, over the births in published cells plus those	county-year cells of the 578 counties that WONDER names in every year in the lightest class	2018 to 2024		2,885 cells	none (an upper limit from the cell count)	CDC WONDER natality
supp_middle	0	cells	count of withheld cells in the five middle birthweight classes	county-year cells of the 578 counties that WONDER names in every year in the five birthweight classes from 2,000 to 4,499 grams	2018 to 2024		20,230 cells	none (full count)	CDC WONDER natality
supp_middle_share	0.0%	percent of cells	share of the cells of the five middle classes that WONDER withholds	county-year cells of the 578 counties that WONDER names in every year in the five birthweight classes from 2,000 to 4,499 grams	2018 to 2024		20,230 cells	none (full count)	CDC WONDER natality
supp_n	11,250	cells	count of cells withheld by WONDER (1 to 9 births)	county-year cells of the 578 counties that WONDER names in every year by birthweight class	2018 to 2024		48,552 cells	none (full count)	CDC WONDER natality
supp_n_cells	48,552	cells	count of county-year-class cells	county-year cells of the 578 counties that WONDER names in every year by birthweight class	2018 to 2024			none (full count)	CDC WONDER natality
supp_n_counties	578	counties	count of counties	counties that WONDER names in every year 2018 to 2024	2018 to 2024			none (full count)	CDC WONDER natality
supp_nostated	65.1%	percent of cells	share of the cells of this class that WONDER withholds	county-year cells of the 578 counties that WONDER names in every year in the class for weight not stated	2018 to 2024		4,046 cells	none (full count)	CDC WONDER natality
supp_overall	23.2%	percent of cells	share of county-year-class cells withheld	county-year cells of the 578 counties that WONDER names in every year by birthweight class	2018 to 2024		48,552 cells	none (full count)	CDC WONDER natality
supp_withheld_light	2,885	cells	count of cells withheld (1 to 9 births)	county-year cells of the 578 counties that WONDER names in every year in the lightest class	2018 to 2024		4,046 cells	none (full count)	CDC WONDER natality
supp_zero_light	350	cells	count of cells published as zero births	county-year cells of the 578 counties that WONDER names in every year in the lightest class	2018 to 2024		4,046 cells	none (full count)	CDC WONDER natality
vsplit_county	15.9	percent of the variance of tract life expectancy	share of the population-weighted variance of tract life expectancy at birth between counties within states (sum-of-squares decomposition)	residents of non-institutional tracts with a life expectancy estimate (50 states and DC)	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts)		78,008 tracts	ACS 2019-2023 tract population	USALEEP life expectancy	Connecticut's nine planning regions stand in for its counties. The tract estimates are modeled; their mean squared standard error equals 26.7 percent of the total variance and sits inside the within-county share.
vsplit_n_counties	3,112	counties and Connecticut planning regions	count of counties with at least one tract in the variance split	residents of non-institutional tracts with a life expectancy estimate (50 states and DC)	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts)			none (full count)	USALEEP life expectancy
vsplit_n_tracts	78,008	tracts	count of tracts in the variance split	residents of non-institutional tracts with a life expectancy estimate (50 states and DC)	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts)			none (full count)	USALEEP life expectancy
vsplit_noise	26.7	percent of the variance of tract life expectancy	population-weighted mean squared standard error of tract life expectancy as a share of the total variance	residents of non-institutional tracts with a life expectancy estimate (50 states and DC)	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts)		78,008 tracts	ACS 2019-2023 tract population	USALEEP life expectancy	The 2020-tract standard errors are mapping grade and understate the error.
vsplit_state	13.7	percent of the variance of tract life expectancy	share of the population-weighted variance of tract life expectancy at birth between states (sum-of-squares decomposition)	residents of non-institutional tracts with a life expectancy estimate (50 states and DC)	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts)		78,008 tracts	ACS 2019-2023 tract population	USALEEP life expectancy	Connecticut's nine planning regions stand in for its counties. The tract estimates are modeled; their mean squared standard error equals 26.7 percent of the total variance and sits inside the within-county share.
vsplit_within	70.3	percent of the variance of tract life expectancy	share of the population-weighted variance of tract life expectancy at birth between tracts within counties (sum-of-squares decomposition)	residents of non-institutional tracts with a life expectancy estimate (50 states and DC)	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts)		78,008 tracts	ACS 2019-2023 tract population	USALEEP life expectancy	Connecticut's nine planning regions stand in for its counties. The tract estimates are modeled; their mean squared standard error equals 26.7 percent of the total variance and sits inside the within-county share.
vsplit_within_signal	60%	percent of the variance net of estimation error	within-county share of the variance of tract life expectancy after subtracting the mean squared standard error from the total and from the within-county part	residents of non-institutional tracts with a life expectancy estimate (50 states and DC)	deaths in 2010 to 2015 (USALEEP, area-weighted onto 2020 tracts)		78,008 tracts	ACS 2019-2023 tract population	USALEEP life expectancy	Takes the published standard errors at face value; the 2020-tract errors are mapping grade and understate the noise, so the share of the true differences that lies within counties is lower.
wonder_county_min	100,000	residents	WONDER names a county only if it has at least this many residents (rule)	counties named individually in CDC WONDER natality	2018 to 2024			none (definition)	CDC WONDER natality
zoo_age_cut	65	years of age	residents of this age and over are counted in the first control (definition)	states and the District of Columbia with at least 60 tracts that have income, the three controls and the measure	BRFSS 2022 (PLACES 2024 release); ACS 2019-2023			none (definition)	American Community Survey
zoo_floor	60	tracts	minimum number of tracts with every variable for a state to enter (rule)	states and the District of Columbia with at least 60 tracts that have income, the three controls and the measure	BRFSS 2022 (PLACES 2024 release); ACS 2019-2023			none (definition)	CDC PLACES
zoo_n_fits	306	regressions	count of state-by-measure regressions	states and the District of Columbia with at least 60 tracts that have income, the three controls and the measure	BRFSS 2022 (PLACES 2024 release); ACS 2019-2023			none (full count)	CDC PLACES
zoo_n_states	51	states	count of states, counting the District of Columbia as one	states and the District of Columbia with at least 60 tracts that have income, the three controls and the measure	BRFSS 2022 (PLACES 2024 release); ACS 2019-2023			none (full count)	CDC PLACES
zoo_neg_five	51	states	count of states where the coefficients for smoking, obesity, diabetes, frequent mental distress and physical inactivity are all below zero	states and the District of Columbia with at least 60 tracts that have income, the three controls and the measure	BRFSS 2022 (PLACES 2024 release); ACS 2019-2023		51 states	none (full count)	CDC PLACES
zoo_r_het	−0.73	correlation coefficient	correlation across states of the smoking coefficient with the state's population-weighted mean smoking prevalence, each state counted once; bootstrap interval over states	adults: current cigarette smoking, states and the District of Columbia with at least 60 tracts that have income, the three controls and the measure	BRFSS 2022 (PLACES 2024 release); ACS 2019-2023	95% CI −0.84 to −0.60	51 states	none (each state counts once)	CDC PLACES
zoo_r_het_rel	−0.18	correlation coefficient	correlation across states of the smoking coefficient divided by the state's population-weighted mean smoking prevalence with that mean, each state counted once; bootstrap interval over states	adults: current cigarette smoking, states and the District of Columbia with at least 60 tracts that have income, the three controls and the measure	BRFSS 2022 (PLACES 2024 release); ACS 2019-2023	95% CI −0.42 to 0.08	51 states	none (each state counts once)	CDC PLACES

Definitions

Area Deprivation Index (ADI): A score that ranks neighborhoods by income, education, employment and housing conditions drawn from the American Community Survey. In this book a higher national percentile means a more deprived neighborhood.
USALEEP: The U.S. Small-area Life Expectancy Estimates Project, a National Center for Health Statistics release of life expectancy at birth for most census tracts. It covers deaths from 2010 to 2015 only and has not been updated.
Census tract: A small area drawn by the Census Bureau to stand in for a neighborhood. Boundaries are redrawn after each decennial census.
Fixed effects: A regression adjustment that gives each group, such as each county, its own baseline. The estimate then rests on differences within groups.
Modeled estimate: A figure produced by a statistical model instead of a direct count or measurement, usually for small areas where direct data are thin, borrowing strength from surveys, neighboring areas or other data. Neighboring areas can share errors, so single extremes call for caution.
Principal components: A method that combines many correlated measures into a few summary scores, ordered by how much of the variation among places each captures. The first component is the single weighted combination that captures the most.
MAUP (modifiable areal unit problem): The same data can show different patterns depending on how the map is divided, for example into tracts, counties or states.
Suppression: The withholding of a number because the count behind it is too small to publish without risking the identification of a person. A suppressed value is missing, not zero.
Ecological inference: Drawing conclusions about individuals from data about places or groups. A pattern across counties need not hold for the people inside them.

Methods

Life expectancy at birth for deaths in 2010 to 2015 is regressed on the 2023 Area Deprivation Index (ADI) across tracts, within counties and within metros, so the outcome and the deprivation measure come from different periods. Tract life expectancy is a modeled estimate area-weighted from 2010 onto 2020 tracts, and the sample is the 77,936 tracts with an ADI rank in the 50 states and DC after institutional tracts are dropped. The variance split has no sampling interval, and the coefficient zoo and the count of withheld birth cells use other sources, described under their own headings.

Which tracts and which years

Tract life expectancy at birth is the USALEEP estimate for deaths in 2010 to 2015, area-weighted from 2010 tracts onto 2020 tracts. Institutional tracts (at least 25 percent of residents in group quarters, or a known case) are dropped, and a tract needs an ACS 2019-2023 population above zero and a county, which leaves 77,936 tracts with an ADI rank (78,008 counting those without one) in the 50 states and DC. Puerto Rico has no USALEEP estimates.

Figure explained here: Figure 1

Measures and weights

Weights are the ACS 2019-2023 tract population. The ADI is the 2023 national percentile (1 least, 100 most deprived).

The nine measures of the index are the poverty rate, the share of households on cash assistance or SNAP, the unemployment rate, the share of adults with a high school diploma or less, the share of households headed by a single parent, the log of median household income, the share of adults with a bachelor's degree or more, labor-force participation and homeownership, from ACS 2019-2023 tracts. Each is standardized across the 82,545 tracts, each counted once; components are the eigenvectors of the correlation matrix.

Figure explained here: Figure 5

Metros, counties and grains of geography

A metro is a core-based statistical area (metropolitan or micropolitan) coded on the Census 2020 county file; each tract takes the area of its county, so Connecticut's planning regions work as counties. A metro needs at least 40 tracts with a life expectancy and an ADI rank.

The six pairs of measures use one sample; county and state values are population-weighted means of tract values, and each correlation is computed weighted by its unit's population and counting each unit once, with bootstrap intervals over counties (tracts) or states (counties and states).

The variance split is the population-weighted sum-of-squares split of b2.canon.variance_split(), computed in the same way for the ADI and the index; it has no sampling interval.

County ranges are population-weighted 10th and 90th percentiles of tract ADI for counties with at least 20 tracts that have an ADI rank; the count of counties, the median range and the correlation of mean ADI with the range are also given at floors of 10 and 40 tracts.

Figures explained here: Figure 2, Figure 8, Figure 6 and Figure 7

The coefficient zoo and withheld birth cells

The coefficient zoo is b2.canon.coefficient_zoo(), the same function the chapter on health behaviors and the supply of care calls: for each state with at least 60 tracts that have every variable, a weighted regression of PLACES prevalence on the log of ACS median household income with the shares of residents ages 65 and over, Black alone and Hispanic as controls, weighted by adults, with county-clustered errors (HC3 where a state has fewer than five counties), times the natural log of 2.

The suppression count uses WONDER natality, county by birthweight class, for the 578 counties named in every year 2018 to 2024; the file lists a withheld cell as missing, and a published 0 is a real zero. Withheld cells are counted and left out; the bound uses only the published threshold of 10 births and the births in published cells.

Figures explained here: Figure 4 and Figure 9

How the slopes and their errors are estimated

Every slope is a weighted least-squares slope of life expectancy on the ADI, times ten. The ladder uses pyfixest with absorbed fixed effects and CRV1 errors, clustered on states for the first two rows and counties for the third, with t intervals on one fewer degree of freedom than there are clusters; the first two are checked against b2.stats.wls with state dummies and the county fit against weighted demeaning.

Each metro slope comes from b2.stats.wls, clustered on counties when the metro has at least five counties and with HC3 errors otherwise.

The variance of the true metro slopes is the variance of the estimated slopes minus their mean squared standard error, and each slope is shrunk toward the mean by that variance over itself plus its squared standard error.

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

Bootstrap intervals, draws and seeds

Metro correlations carry bootstrap intervals over metros (2,000 draws, seed 12345).

Intervals for eigenvalues, loadings, alpha and the correlation come from a bootstrap over counties (2,000 draws, seed 12345) that recomputes the correlation matrix. Rank correlations hold ranks fixed in the bootstrap.

What these data cannot show

The metro slopes cover the residents of metros with enough tracts and say nothing about the rest. Their intervals use HC3 errors in most metros and may be too narrow, which would make the share of the spread that is estimation error larger. The ADI is a national percentile and understates deprivation in high-cost cities (Chapter 1.1, What the deprivation index measures (https://vishalsingh.org/zipcode/anatomy-of-deprivation)), which bears on the metro slopes. Puerto Rico is not covered, and Connecticut's planning regions stand in for its counties in the metros, the variance split and the county ranges.

References

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
Arias, Elizabeth; Escobedo, Luis A.; Kennedy, Jiaquan; Fu, Chenxi; Cisewski, Jodi (2018). U.S. Small-area Life Expectancy Estimates Project: Methodology and Results Summary. Vital and Health Statistics, Series 2, Number 181, National Center for Health Statistics. www.cdc.gov/nchs/data/series/sr_02/sr02_181.pdf
Cameron, A. Colin; Miller, Douglas L. (2015). A Practitioner's Guide to Cluster-Robust Inference. Journal of Human Resources 50(2): 317-372. doi:10.3368/jhr.50.2.317
Greenlund, Kurt J.; Lu, Hua; Wang, Yan; Matthews, Kevin A.; LeClercq, Jennifer M.; Lee, Benjamin; Carlson, Susan A. (2022). PLACES: Local Data for Better Health. Preventing Chronic Disease 19: 210459. doi:10.5888/pcd19.210459
Messer, Lynne C.; Laraia, Barbara A.; Kaufman, Jay S.; Eyster, Janet; Holzman, Claudia; Culhane, Jennifer; Elo, Irma; Burke, Jessica G.; O'Campo, Patricia (2006). The Development of a Standardized Neighborhood Deprivation Index. Journal of Urban Health 83(6): 1041-1062. doi:10.1007/s11524-006-9094-x
Kaiser, Henry F. (1960). The Application of Electronic Computers to Factor Analysis. Educational and Psychological Measurement 20(1): 141-151. doi:10.1177/001316446002000116
Cronbach, Lee J. (1951). Coefficient Alpha and the Internal Structure of Tests. Psychometrika 16(3): 297-334. doi:10.1007/BF02310555
Openshaw, Stan (1984). The Modifiable Areal Unit Problem. CATMOG 38, Concepts and Techniques in Modern Geography, Geo Books, Norwich.
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

Provenance

Pipeline version: 0.1.0
Build script: book2/chapters/gradients-slopes-and-variance/build.py (sha256 e3c0eb69d17fd0c8f4f36ceab0c447935b0800f5cb97899e4c225561ef54b28a)
Built on: 2026-10-07
Site commit: c2b1ff3309cb4a10833f884da91ac0057a66ad0a

Inputs

File	Size (bytes)	SHA-256, first 1 MB	SHA-256, last 1 MB
book2/chapters/gradients-slopes-and-variance/f_pca.py	9,345	5353ebb9cba70e3b87433e822f6caa660a2b724c02b4d6e0cfe56e455575bf30	5353ebb9cba70e3b87433e822f6caa660a2b724c02b4d6e0cfe56e455575bf30
book2/chapters/gradients-slopes-and-variance/f_sample.py	3,332	68db6c906b0076863c96cc3ae3b2ca569e20a783b4043a6a278cbcf78a298192	68db6c906b0076863c96cc3ae3b2ca569e20a783b4043a6a278cbcf78a298192
book2/chapters/gradients-slopes-and-variance/f_scale.py	7,541	21813ba176531a15307e31ddac981de8b5383e726f3cba3b569ff001639e1545	21813ba176531a15307e31ddac981de8b5383e726f3cba3b569ff001639e1545
book2/chapters/gradients-slopes-and-variance/f_slopes.py	8,806	4a4adf3f306b504412faa7538c4ce2983dce6f1006e7e07e8e0d4c4450c5ba6c	4a4adf3f306b504412faa7538c4ce2983dce6f1006e7e07e8e0d4c4450c5ba6c
book2/chapters/gradients-slopes-and-variance/f_supp.py	3,227	d06aab1dbc3521d9f5243f092021f968582f4168792e14960dc2aa62a0447017	d06aab1dbc3521d9f5243f092021f968582f4168792e14960dc2aa62a0447017
data/analytic/tract_atlas.parquet	39,659,925	29d5e4d943552d54aeb6ba92aadc939d9bd15bdea553c78357dbd516757be627	1e10d1ecf722340f23ee2212d4999a90250f85b65fa89b39e32db06d9472e22e
data/geo/counties_2020.parquet	123,034,155	2fce837cdc2db1f2c229f2ea453c6793338a462770307e2c64e8d1fd713ec663	dae928bf1042f3d174d2d64d6e3ec923fa3daa40aee1bb4feb104aac2fe3c04a
data/geo/dim_geo.parquet	1,180,921	29d5e4d943552d54aeb6ba92aadc939d9bd15bdea553c78357dbd516757be627	e58c8c488df54ab3187f5461f179322afdc49c9bd2315404003fe1272eea32c3
data/staged/adi/adi_atlas_tract2020_2023.parquet	798,078	5e409bc212ff871def780fa27b4cf8b616d9da420b2b9f6455517eddcd8c1a95	5e409bc212ff871def780fa27b4cf8b616d9da420b2b9f6455517eddcd8c1a95
data/staged/births/wonder_natality_county_birthweight_2018_2024.parquet	115,259	17a46844906a1ab03d02c98fd4e2aca0535a3ad4da186e0feaafb17a03dfa9ef	17a46844906a1ab03d02c98fd4e2aca0535a3ad4da186e0feaafb17a03dfa9ef
data/staged/births/wonder_natality_county_total_2018_2024.parquet	41,774	e85d351fa72c022a05415d332185113b6badcaff1119da1d6f45c76ffe16d86d	e85d351fa72c022a05415d332185113b6badcaff1119da1d6f45c76ffe16d86d
data/staged/health/places_tract2020_latest.parquet	5,919,159	af4c3b0d1205b7d84b81676a9529ef73323b2dbeb9d54e235f5efeebfad83177	a2f19029ee2ef032be87e0d5288d88d489afd52379279833f178baa021df3dbf
data/staged/life_expectancy/usaleep_tract2020.parquet	1,316,169	9c9c7f103791b75eddba74e68a89804efdf2486b6b66bb4bc02a46ba5bf8bc23	27317107be2465d9fd5e83271dea0cd6e915ee419e2802bedc28747d0a41d38f
data/staged/ses/acs5_tract2020_2019_2023.parquet	7,232,424	5fe6f730a35225f14d5d8abfc6b9bce58b52e545f309a7eac74f1dae74d7a31c	7b4a2475b3852d3f91804716a04566ef8ceadacec2be9fb85f5eb642078efac6
data/staged/svi/svi_tract2020_2022.parquet	17,740,415	cb3860d8edc2aaf1f2a02211a58f7a0b80457eb6ad9a37b5ec8abb2d5da9da10	e371a4a696d5bed05d54ad7c5b6ca2504508805b847ec415c2322be7f66ec2b2

Outputs

File	SHA-256
facts.json	318e84614c33eaa882eec84cd8bce9f60c8f355f3c67c98d5a29aa26cdeb93cf
figures/coefficient_zoo.json	644555fe41b4157a0841b8a37c8d543273ce469b0dc3067d4346bb97acf31bf0
figures/county_adi_spread.json	f6454ca1e281f5e8a649b5cabc08c85c2d61abf44445b66e7a693af5efa409dd
figures/fe_ladder.json	e5477b2b5672b53bb02a5401ff3d4fce98dc85b843e74b98caa800bf0adf851b
figures/maup_correlations.json	28cce465fe4429b41eaaa7d47597135a041799e7e041e5c7eafea7f50d909d87
figures/metro_slopes_forest.json	261d8e3e45f05da6ad4da94af932eadcde69f8d9be7511468f31fe36d14f38b8
figures/nine_measure_pca.json	5e29e1ac6c3b2dff95909ae56e795abe1ff8f6326fe6652cde3b6c2b8a32f39a
figures/slope_heterogeneity.json	1a3f5ebd417cf63a702c258822e622bfee7a821a7c64866cc9b4c428a7710da6
figures/variance_split.json	5f6d353f9319dc62e2a886546d14398a61bf5d366ce1e59f01ce123f591d94c5
figures/wonder_suppression_by_birthweight.json	2c3e6b55c3719f5c39bcb5fb395dab64e63ddd20d0a3bfde02e079b93c545a5a
meta.json	dee469e5a873abf617d4405e8998402c4464d7cfe72cb4002f96a7743ccebfdc
refs.json	ac8e900619c72fda0850597fc4e7b43a50aa28644f2322fa3fdeabad2f039f8f

Machine formats

Report (Markdown): https://vishalsingh.org/zipcode/gradients-slopes-and-variance/report.md
Every estimate (JSON): https://vishalsingh.org/zipcode/report/gradients-slopes-and-variance/facts.json
Table 1, figure "fe_ladder" (JSON): https://vishalsingh.org/zipcode/report/gradients-slopes-and-variance/figures/fe_ladder.json
Table 2, figure "metro_slopes_forest" (JSON): https://vishalsingh.org/zipcode/report/gradients-slopes-and-variance/figures/metro_slopes_forest.json
Table 3, figure "slope_heterogeneity" (JSON): https://vishalsingh.org/zipcode/report/gradients-slopes-and-variance/figures/slope_heterogeneity.json
Table 4, figure "coefficient_zoo" (JSON): https://vishalsingh.org/zipcode/report/gradients-slopes-and-variance/figures/coefficient_zoo.json
Table 5, figure "nine_measure_pca" (JSON): https://vishalsingh.org/zipcode/report/gradients-slopes-and-variance/figures/nine_measure_pca.json
Table 6, figure "maup_correlations" (JSON): https://vishalsingh.org/zipcode/report/gradients-slopes-and-variance/figures/maup_correlations.json
Table 7, figure "variance_split" (JSON): https://vishalsingh.org/zipcode/report/gradients-slopes-and-variance/figures/variance_split.json
Table 8, figure "county_adi_spread" (JSON): https://vishalsingh.org/zipcode/report/gradients-slopes-and-variance/figures/county_adi_spread.json
Table 9, figure "wonder_suppression_by_birthweight" (JSON): https://vishalsingh.org/zipcode/report/gradients-slopes-and-variance/figures/wonder_suppression_by_birthweight.json
Index of every report: https://vishalsingh.org/zipcode/llms.txt; all reports in one file: https://vishalsingh.org/zipcode/llms-full.txt

Identification

Principal estimates

Findings

The slope is steeper within counties than across all tracts

Life expectancy is lower in more deprived tracts in every metro

Metro traits are not detectably related to the spread

The coefficient zoo returns coefficients of both signs

A nine-measure index keeps one component and discards a second

A correlation depends on the size of the area

Most variation in life expectancy lies between tracts in the same county

Large counties differ in how much of the ADI scale their tracts cover

Suppressed cells are not zeros

What these data cannot show

Tables

Table 1. Life expectancy is 1.12 years lower per ten-point step of the ADI within counties, against 0.84 years across all tracts

Table 2. Life expectancy is lower in more deprived tracts in all 267 metros, by a median of 1.20 years per ten-point step

Table 2.1. Distribution

Table 2.2. Slope by metro

Table 3. Metro slopes are not detectably related to metro median income, a correlation of +0.05 counting each metro once

Table 4. The same regression in every state gives coefficients below zero for five measures in all 51 states and above zero for binge drinking in 50

Table 5. The first of nine components holds 51.3% of the variance in nine census measures and the second holds 16.0%

Table 5.1. Scree

Table 5.2. First component

Table 5.3. Second component

Table 6. Weighted by population, the same pair of measures has a different correlation at each grain, and homeownership and poverty go from −0.53 across tracts to −0.05 across states

Table 7. 70.3 percent of the variance of tract life expectancy lies between tracts in the same county, 26.7 points of it estimation error, against 34% percent for the ADI

Table 8. In each of the 30 most populous counties, tracts at the 10th and 90th percentile of the ADI are between 9 and 69 percentile points apart

Table 9. WONDER withholds 71.3% of county-year cells in the lightest birthweight class and 77.6% in the heaviest, against 0.0% in the five middle classes

All estimates

Definitions

Methods

Which tracts and which years

Measures and weights

Metros, counties and grains of geography

The coefficient zoo and withheld birth cells

How the slopes and their errors are estimated

Bootstrap intervals, draws and seeds

What these data cannot show

References

Provenance

Machine formats