# Gradients, slopes and variance: report

Chapter 10.1 of ZIP Code Destiny, by Vishal Singh (NYU Stern). Generated from the chapter's data files; the reading version is at https://vishalsingh.org/zipcode/gradients-slopes-and-variance. Table n of this report is figure n of the chapter.

## 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: 2026-10-09
- 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
- Report: https://vishalsingh.org/zipcode/gradients-slopes-and-variance/report
- Markdown: https://vishalsingh.org/zipcode/gradients-slopes-and-variance/report.md
- BibTeX key: singh2026gradientsslopesandvariance

```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.

| 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 (https://vishalsingh.org/zipcode/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

### Metro traits are not detectably related to the spread

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 (https://vishalsingh.org/zipcode/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 (https://vishalsingh.org/zipcode/anatomy-of-deprivation) 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 (https://vishalsingh.org/zipcode/deprivation-at-two-zooms) rebuilds a deprivation index for counties and Chapter 10.2, Families of places (https://vishalsingh.org/zipcode/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 (https://vishalsingh.org/zipcode/anatomy-of-deprivation) 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 (https://vishalsingh.org/zipcode/birthweight-and-preterm-birth) uses this file, and Chapter 10.3, Suppressed counties in death data (https://vishalsingh.org/zipcode/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 (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.

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: https://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: https://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: https://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: https://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: https://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: https://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: https://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: https://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: https://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.

| 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.

Explains Table 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.

Explains Table 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.

Explains Table 2, Table 8, Table 6 and Table 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.

Explains Table 4 and Table 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.

Explains Table 1, Table 2 and Table 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

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 (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.

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.

## References

1. 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. https://doi.org/10.1056/NEJMp1802313
2. 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. https://www.cdc.gov/nchs/data/series/sr_02/sr02_181.pdf
3. Cameron, A. Colin; Miller, Douglas L. (2015). A Practitioner's Guide to Cluster-Robust Inference. Journal of Human Resources 50(2): 317-372. https://doi.org/10.3368/jhr.50.2.317
4. 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. https://doi.org/10.5888/pcd19.210459
5. 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. https://doi.org/10.1007/s11524-006-9094-x
6. Kaiser, Henry F. (1960). The Application of Electronic Computers to Factor Analysis. Educational and Psychological Measurement 20(1): 141-151. https://doi.org/10.1177/001316446002000116
7. Cronbach, Lee J. (1951). Coefficient Alpha and the Internal Structure of Tests. Psychometrika 16(3): 297-334. https://doi.org/10.1007/BF02310555
8. Openshaw, Stan (1984). The Modifiable Areal Unit Problem. CATMOG 38, Concepts and Techniques in Modern Geography, Geo Books, Norwich.
9. Robinson, W. S. (1950). Ecological Correlations and the Behavior of Individuals. American Sociological Review 15(3): 351-357. https://doi.org/10.2307/2087176
10. 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. https://doi.org/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 |
| --- | ---: | --- | --- |
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| 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 |
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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 (HTML): https://vishalsingh.org/zipcode/gradients-slopes-and-variance/report
- 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