Protocol - Neighborhood Concentrated Disadvantage

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Protocol Name from Source:

U.S. Census Bureau, 1990 and 2000 Decennial Censuses (SF3), and American Community Survey (ACS), 5-year estimates, 2005-2009 to 2010-2014.


Publicly available


The protocol is based on extracting data from the U.S. Census Bureau on a set of variables related to the concept of "concentrated disadvantage" (Sampson, Raudenbush, & Earls, 1997). All the relevant variables are available from the long form of the 1990 and 2000 Decennial Censuses (SF3) and from the 5-year American Community Survey (ACS) estimates. ACS estimates are annually updated; current 5-year data sets range from 2005–2009 to 2010–2014. Once the data are extracted, an index score of concentrated disadvantage can be calculated at the neighborhood level of interest; this is usually based on census tract or census block group data.


Accessing and Understanding the American Community Survey (ACS Data

The ACS data used in this protocol can be accessed by using Excel to read the Summary Files or using the “Download Center” at the U.S. Census Bureau’s American FactFinder portal ([link[factfinder.census.gov|http://factfinder.census.gov]]). Users can find additional information on these tools at the following locations:

Using Excel to Access Summary Files: [link[www2.census.gov/programs-surveys/acs/summary_file/2014/documentation/tech_docs/ACS_SF_Excel_Import_Tool.pdf|http://www2.census.gov/programs-surveys/acs/summary_file/2014/documentation/tech_docs/ACS_SF_Excel_Import_Tool.pdf]]

Using the Download Center: [link[www2.census.gov/programs-surveys/acs/summary_file/2014/documentation/tech_docs/How_to_Access_ACS_Estimates_AFF.pdf|http://www2.census.gov/programs-surveys/acs/summary_file/2014/documentation/tech_docs/How_to_Access_ACS_Estimates_AFF.pdf]]

The technical documentation for the American Community Survey (ACS) summary files is available online at [link[www.census.gov/programs-surveys/acs/technical-documentation.html|http://www.census.gov/programs-surveys/acs/technical-documentation.html]]. Select the “Summary File Documentation” link, and then select the data set of interest. Users not familiar with Census data should consult the technical materials.

If the user is interested in additional variables beyond those included in the neighborhood concentrated disadvantage protocol, they should be aware that not all ACS estimates are available for all geographies. These missing estimates are due to data suppression techniques by which the U.S. Census Bureau limits disclosure of individual data and does not release estimates with poor statistical reliability. Additional information about data suppression and the specific estimates it impacts can be found at [link[www.census.gov/programs-surveys/acs/technical-documentation/data-suppression.html|http://www.census.gov/programs-surveys/acs/technical-documentation/data-suppression.html]].

Although block group data have long been available from the Census File Transfer Protocol site, the Census Bureau did not make block groups available for download at American FactFinder until the release of the 2009-2013 ACS. Information about accessing block group data for earlier years is available at [link[www.census.gov/library/video/acs_block_group.html|http://www.census.gov/library/video/acs_block_group.html]].

Calculating Neighborhood Concentrated Disadvantage

Concentrated disadvantage is derived from six Census variables:

1. Percent of Individuals Below the Poverty Line (derived from ACS Table C17002)

2. Percent of Households Receiving Public Assistance (derived from ACS Table B19057)

3. Percent Female-Headed Families (derived from ACS Table B11001)

4. Percent Unemployed (derived from ACS Table B23025)

5. Percent Less Than Age 18 (derived from ACS Table B01001)

6. Percent Black (derived from ACS Table B02001)

Concentrated disadvantage is calculated for all subareas within a study area.

While some commercial data products may include the derivation of some of these variables, the detailed material below is based on the assumption that the user will go to the U.S. Census Bureau (original source) for all the raw data counts needed to calculate the individual variables that create the measure Concentrated Disadvantage. The protocol text uses the unique ID of individual variables. These descriptions can be found in the “Table Shells” download on the Summary File Technical Documentation (available here [link[www.census.gov/programs-surveys/acs/technical-documentation/summary-file-documentation.2014.html|http://www.census.gov/programs-surveys/acs/technical-documentation/summary-file-documentation.2014.html]]). Note: users who download tables from the Download Center will receive an extract containing a unique Excel file for each table requested. The tables do not use the unique ID of the variables presented in the summary files but do contain header data that describe the variable.

1: "Percent of Individuals Below the Poverty Line" is derived from data in ACS 5-Year “Table C17002: Ratio of Income to Poverty Level in the Past 12 Months.”

Table C17002: Ratio of Income to Poverty Level in the Past 12 Months

Universe: Population for whom poverty status is determined.

There are eight variables included in table C17002 (see line 14192 of the ACS2014_Table_Shells.xlsx file available in the Technical Documentation).

Table C17002: Ratio of Income to Poverty Level in the Past 12 Months is reproduced below:

Variable Code

Variable Name




Under .50


.50 to .99


1.00 to 1.24


1.25 to 1.49


1.50 to 1.84


1.85 to 1.99


2.00 and over

The percent of individuals below the poverty line = [(C17002002 + C17002003)/C17002001] x 100.

2: "Percent of Households Receiving Public Assistance" is derived from ACS "Table B19057: Public Assistance Income in the Past 12 Months for Households.”

Table B19057: Public Assistance Income in the Past 12 Months for Households

Universe: Households.

There are three variables included in Table B19015. Table B19015 is reproduced below:

Variable Code

Variable Name




With public assistance income


No public assistance income

The “percent of households on public assistance” = (B19057002/B19057001) * 100.

From the ACS Summary File Subject Definitions, public assistance income “includes general assistance and Temporary Assistance to Needy Families (TANF). Separate payments received for hospital or other medical care (vendor payments) are excluded. This does not include Supplemental Security Income (SSI) or noncash benefits such as Food Stamps” (p. 81 of 2014 Subject Definitions document).

3: "Percent Female-Headed Families" is derived from ACS “Table B11001: Household Type (Including Living Alone).”

There are nine cells in Table B1101. The table is reproduced below:

Variable Code

Variable Name




Family households:


  Married-couple family


  Other family:


    Male householder, no wife present


    Female householder, no husband present


Nonfamily households:


  Householder living alone


  Householder not living alone

The “percent of female-headed families” = (B11001006/B11001001) * 100.

4: "Percent Unemployed" is derived from ACS "Table B23025: Employment Status for the Population 16 Years and Over."

Table B23025: Employment Status for the Population 16 Years and Over

Universe: Population 16 years and over.

From the 2014 Subject Definitions document (p. 63), the U.S. Census Bureau definition of being unemployed is the following:

"All civilians 16 years old and over are classified as unemployed if they (1) were neither ‘at work’ nor ‘with a job but not at work’ during the reference week, and (2) were actively looking for work during the last 4 weeks, and (3) were available to start a job. Also included as unemployed are civilians who did not work at all during the reference week, were waiting to be called back to a job from which they had been laid off, and were available for work except temporary illness. Examples of job-seeking activities are: registering at a public or private employment office; meeting with prospective employers; investigating possibilities for starting a professional practice or opening a business; placing or answering advertisements; writing letters of application; being on a union or professional register."

Table B23025 contains seven cells. The table is reproduced below.

Variable Code

Variable Name




  In labor force:


    Civilian labor force:






    Armed Forces


  Not in labor force

The "percent unemployed" = ([B23025005 + B23025003]/P43001) x 100.

5: "Percent Less Than Age 18" is derived from ACS "Table B01001: Sex by Age."

Table B01001: Sex by Age

Universe: Total Population.

There are 49 cells in ACS Table B01001 (2014_Table_Shells.xlsx).

Users need to combine the counts for both males and females. Thus, the sum of males under age 18 years old (from under 5 years old to 15-17 years old) equals the sum of all cells B01001002 through B01001006 and for females the sum of all cells B01001027 through B01001030.

The "percent less than age 18" =

([(B01001002: B01001006) + (B01001027: B01001030)]/B01001001) * 100

6: "Percent Black" is derived from ACS "Table B02001: Race."

Table B02001: Race

Universe: Total population.

There are 10 cells in Table B02001 (reproduced below):

Variable Code

Variable Name




  White alone


  Black or African American alone


  American Indian and Alaska Native alone


  Asian alone


  Native Hawaiian and Other Pacific Islander alone


  Some other race alone


  Two or more races:


    Two races including Some other race


    Two races excluding Some other race, and three or more races

The "Percent Black" = (B02001003/B02001001) * 100

Personnel and Training Required

Knowledge of Census data products and websites, such as American Factfinder, and/or publicly available data portals (e.g., National Historical Geographic Information System), and/or commercial geospatial data products, such as that provided by vendors like GeoLytics or Social Explorer.

After extracting the necessary data, statistical methods are used (e.g., principal component analysis [PCA] and factor analysis).

Equipment Needs

Access to a desktop/laptop computer with Internet access to download raw data from the U.S. Census Bureau’s American Factfinder website. Statistical packages (e.g., SPSS, SAS) for data manipulation and factor analysis.


Requirement CategoryRequired
Average time of greater than 15 minutes in an unaffected individualNo
Major equipmentNo
Specialized requirements for biospecimen collectionNo
Specialized trainingNo

Mode of Administration


Life Stage:

Infant, Toddler, Child, Adolescent, Adult, Senior, All Ages, Pregnancy

Specific Instructions:

Assuming that information on current address (see PhenX Demographics domain, Current Address measure) has been collected for a study respondent, then it is possible to use geocoding to link the address of a study participant to his or her local neighborhood (a geographic area), typically by a Census-defined area, such as a census block group or a census tract or by Zone Improvement Plan (ZIP) code area (captured by the U.S. Census Bureau as a ZIP Code Tabulation Area [ZCTA]).

The original paper by Sampson et alia (1997) was based on the use of variables from the 1990 Decennial Census and applied to a neighborhood definition based on aggregates of Census tracts, called neighborhood clusters.

The Social Environments Working Group (WG) recommends that researchers follow Sampson et alia (1997) and conduct a factor analysis (e.g., a principal components analysis using varimax rotation methods or alpha-scoring factor analysis).The extracted variables are typically very highly correlated, undermining any investigation of unique effects. Sampson et alia (1997, p. 920) find that, consistent with urban theory, these six poverty-related variables are highly associated and load on the same factor (note: their work was based on 1990 Census data for Chicago). Other studies in other settings confirm that these variables (poverty, percentage of single-parent families, percentage of family members on welfare and unemployed, and a measure of racial segregation) load on a single factor with individual factor loadings typically exceeding 0.8.

The Social Environments WG recommends that investigators record and report the factor loading scores for each variable used in the factor analysis. These would vary across studies, but knowing how they vary (i.e., what other studies found) would allow for comparison between studies. Depending on the purpose of the study, investigators may want to remove the measure of Percent Black from the scale if the unique effects of racial concentration are a key research emphasis.

The calculation of concentrated disadvantage based on factor analysis generates a measure that is sample dependent (i.e., study specific). However, it is important to note that this is a well-established, robust, and highly cited measure across the social sciences and public health. The social science literature has long argued that neighborhood disadvantage is not a single-item construct captured by, for example, a measure of poverty (e.g., percentage of individuals below the poverty level) or measures such as the Index of Concentration at the Extremes (Massey, 2001).

Research Domain Information

Release Date:

May 31, 2016


This measure uses readily available secondary data from the U.S. Census Bureau.


This measure examines various population characteristics at the neighborhood level to determine the concentration of poverty. In the social science and public health literatures, one of the most important indicators for a host of individual outcome measures that are incorporated at the neighborhood level is Neighborhood Concentrated Disadvantage.

Selection Rationale

The Social Environments Working Group preferred an objective measure using U.S. Census Bureau data over a questionnaire that would rely on subjective judgment based on retrospective ascertainment, which is likely to be unreliable. Additionally, the measure of "concentrated disadvantage" is derived from the work of Sampson and colleagues (1997) on the Project on Human Development in Chicago Neighborhoods (PHDCN), which is a well-known, large-scale study.

The measure has been used in numerous papers including, the highly cited (3,000+ citations) paper by Sampson et al. (1997).




Common Data Elements (CDE)Social Environment Neighborhood Concentrated Disadvantage Assessment Score3150986CDE Browser
Logical Observation Identifiers Names and Codes (LOINC)Neighborhood disadvantage proto63036-8LOINC

Process and Review

The [link[www.phenx.org/node/102|Expert Review Panel #2]] (ERP 2) reviewed the measures in the Demographics, Social Environments and Environmental Exposures domains.

Guidance from ERP 2 includes:

• Replaced protocol

• New Data Dictionary

Back-compatible: there are changes to the Data Dictionary, previous version of the Data Dictionary and Variable mapping in Toolkit archive ([link[www.phenxtoolkit.org/index.php?pageLink=browse.archive.protocols&id=210000|link]])


Recommended data sources include the following:

The U.S. Census Bureau decennial Census (1990 and 2000).

American Factfinder, http://factfinder.census.gov

American Community Survey (ACS) products (specifically, the 5-year estimates), http://www.census.gov/programs-surveys/acs.

Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 277(5238), 918-924.

General References

Kawachi, I., & Berkman, L. (2003). Neighborhoods and health. New York: Oxford University Press.

Massey, D. S. (2001). The prodigal paradigm returns: ecology comes back to sociology. In: Booth A, Crouter A, eds. Does It Take a Village? Community Effects on Children, Adolescents, and Families. Mahwah, NJ: Lawrence Erlbaum Associates; 41-48.

Massey, D. S., & Denton, N. (1993). American apartheid: Segregation and the making of the underclass. Cambridge, MA: Harvard University Press.

Sampson, R. J., Morenoff, J., & Gannon-Rowley, T. (2002). Assessing neighborhood effects: Social processes and new directions in research. Annual Review of Sociology, 28, 443-478.

Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 277(5238), 918-924.

Wilson, W. J. (1987). The truly disadvantaged: The inner city, the underclass, and public policy. Chicago: University of Chicago Press.

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