Washington DC street

Using the ACS to Calculate Daytime Population

I’m in the home stretch for getting the last chapter of the first draft of my census book completed. The next to last chapter of the book provides an overview of a number of derivatives that you can create from census data, and one of them is the daytime population.

There are countless examples of using census data for site selection analysis and for comparing and ranking places for locating new businesses, providing new public services, and generally measuring potential activity or population in a given area. People tend to forget that census data measures people where they live. If you were trying to measure service or business potential for residents, the census is a good source.

Counts of residents are less meaningful if you wanted to gauge how crowded or busy a place was during the day. The population of an area changes during the day as people leave their homes to go to work or school, or go shopping or participate in social activities. Given the sharp divisions in the US between residential, commercial, and industrial uses created by zoning, residential areas empty out during the weekdays as people travel into the other two zones, and then fill up again at night when people return. Some places function as job centers while others serve as bedroom communities, while other places are a mixture of the two.

The Census Bureau provides recommendations for calculating daytime population using a few tables from the American Community Survey (ACS). These tables capture where workers live and work, which is the largest component of the daytime population.

Using these tables from the ACS:

Total resident population
B01003: Total Population
Total workers living in area and Workers who lived and worked in same area
B08007: Sex of Workers by Place of Work–State and County Level (‘Total:’ line and ‘Worked in county of residence’ line)
B08008: Sex of Workers by Place of Work–Place Level (‘Total:’ line and ‘Worked in place of residence’ line)
B08009: Sex of Workers by Place of Work–Minor Civil Division Level (‘Total:’ line and ‘Worked in MCD of residence’ line)
Total workers working in area
B08604: Total Workers for Workplace Geography

They propose two different approaches that lead to the same outcome. The simplest approach: add the total resident population to the total number of workers who work in the area, and then subtract the total resident workforce (workers who live in the area but may work inside or outside the area):

Daytime Population = Total Residents + Total Workers in Area - Total Resident Workers

For example, according to the 2017 ACS Washington DC had an estimated 693,972 residents (from table B01003), 844,345 (+/- 11,107) people who worked in the city (table B08604), and 375,380 (+/- 6,102) workers who lived in the city. We add the total residents and total workers, and subtract the total workers who live in the city. The subtraction allows us to avoid double counting the residents who work in the city (as they are already included in the total resident population) while omitting the residents who work outside the city (who are included in the total resident workers). The result:

693,972 + 844,345 - 375,380 = 1,162,937

And to get the new margin of error:

SQRT(0^2 + 11,107^2 + 6,102^2) = 12,673

So the daytime population of DC is approx 468,965 people (68%) higher than its resident population. The district has a high number of jobs in the government, non-profit, and education sectors, but has a limited amount of expensive real estate where people can live. In contrast, I did the calculation for Philadelphia and its daytime population is only 7% higher than its resident population. Philadelphia has a much higher proportion of resident workers relative to total workers. Geographically the city is larger than DC and has more affordable real estate, and faces stiffer suburban competition for private sector jobs.

The variables in the tables mentioned above are also cross-tabulated in other tables by age, sex, race, Hispanic origin , citizenship status, language, poverty, and tenure, so it’s possible to estimate some characteristics of the daytime population. Margins of error will limit the usefulness of estimates for small population groups, and overall the 5-year period estimates are a better choice for all but the largest areas. Data for workers living in an area who lived and worked in the same area is reported for states, counties, places (incorporated cities and towns), and municipal civil divisions (MCDs) for the states that have them.

Data for the total resident workforce is available for other, smaller geographies but is reported for those larger places, i.e. we know how many people in a census tract live and work in their county or place of residence, but not how many live and work in their tract of residence. In contrast, data on the number of workers from B08604 is not available for smaller geographies, which limits the application of this method to larger areas.

Download or explore these ACS tables from your favorite source: the American Factfinder, the Census Reporter, or the Missouri Census Data Center.

Lying with Maps and Census Data

I was recently working on some examples for my book where I discuss how census geography and maps can be used to intentionally skew research findings. I suddenly remembered Mark Monmonier’s classic How To Lie with Maps. I have the 2nd edition from 1996, and as I was adding it to my bibliography I wondered if there was a revised edition.

To my surprise, a 3rd edition was just published in 2018! This is an excellent book: it’s a fun and easy read that provides excellent insight into cartography and the representation of data with maps. There are concise and understandable explanations of classification, generalization, map projections and more with lots of great examples intended for map readers and creators alike. If you’ve never read it, I’d highly recommend it.

If you have read the previous edition and are thinking about getting the new one… I think the back cover’s tagline about being “fully updated for the digital age” is a little embellished. I found another reviewer who concurs that much of the content is similar to the previous edition. The last three chapters (about thirty pages) are new. One is devoted to web mapping and there is a nice explanation of tiling and the impact of scale and paid results on Google Maps. While the subject matter is pretty timeless, some more updated examples would have been welcome.

There are many to choose from. One of the examples I’m using in my book comes from a story the Washington Post uncovered in June 2017. Jared Kushner’s real estate company was proposing a new luxury tower development in downtown Jersey City, NJ, across the Hudson River from Manhattan. They applied for a program where they could obtain low interest federal financing if they built their development in an area were unemployment was higher than the national average. NJ State officials assisted them with creating a map of the development area, using American Community Survey (ACS) unemployment data at the census tract level to prove that the development qualified for the program.

The creation of this development area defies all logical and reasonable criteria. This affluent part of the city consists of high-rise office buildings, residential towers, and historic brownstones that have been refurbished. The census tract where the development is located is not combined with adjacent tracts to form a compact and contiguous area that functions as a unit, nor does it include surrounding tracts that have similar socio-economic characteristics. The development area does not conform to any local conventions as to what the neighborhoods in Jersey City are based on architecture, land use, demographics, or physical boundaries like major roadways and green space.

Jersey City Real Estate Gerrymandering Map

Census tracts that represent the “area” around a proposed real estate development were selected to concentrate the unemployed population, so the project could qualify for low interest federal loans.

Instead, the area was drawn with the specific purpose of concentrating the city’s unemployed population in order to qualify for the financing. The tract where the development is located has low unemployment, just like the tracts around it (that are excluded). It is connected to areas of high unemployment not by a boundary, but by a single point where it touches another tract diagonally across a busy intersection. The rest of the tracts included in this area have the highest concentration of unemployment and poverty in the city, and consists primarily of low-rise residential buildings, many of which are in poor condition. This area stretches over four miles away from the development site and cuts across several hard physical boundaries, such as an interstate highway that effectively separates neighborhoods from each other.

The differences between this development area and the actual area adjacent but excluded from the project couldn’t be more stark. Gerrymandering usually refers to the manipulation of political and voting district boundaries, but can also be used in other contexts. This is a perfect example of non-political gerrymandering, where areas are created based on limited criteria in order to satisfy a predefined outcome. These areas have no real meaning beyond this purpose, as they don’t function as real places that have shared characteristics, compact and contiguous boundaries, or a social structure that would bind them together.

The maps in the Post article high-lighted the tracts that defined the proposal area and displayed their unemployment rate. In my example I illustrate the rate for all the tracts in the city so you can clearly see the contrast between the areas that are included and excluded. What goes unmentioned here is that these census ACS estimates have moderate to high margins of error that muddy the picture even further. Indeed, there are countless ways to lie with maps!

Business and Labor Force Data: The Census and the BLS

I’m still cranking away on my book, which will be published by SAGE Publications and is tentatively titled Exploring the US Census: Your Guide to America’s Data. I’m putting the finishing touches on the chapter devoted to business datasets.

Most of the chapter is dedicated to the Census Bureau’s (CB) Business Patterns and the Economic Census. In a final section I provide an overview of labor force data produced by the Bureau of Labor Statistics (BLS). At first glance these datasets appears to cover a lot of the same ground, but they do vary in terms of methodology, geographic detail, number of variables, and currency / frequency of release. I’ll provide a summary of the options in this post.

The Basics

Most of these datasets provide data for business establishments, which are individual physical locations where business is conducted or where services or industrial operations are performed, and are summarized by industries, which are groups of businesses that produce similar products or provide similar services. The US federal government uses the North American Industrial Classification System (NAICS), a hierarchical series of codes used to classify businesses and the labor force into divisions and subdivisions at varying levels of detail.

Since most of these datasets are generated from counts, surveys, or administrative records for business establishments they summarize business activity and the labor force based on where people work, i.e. where the businesses are. The Current Population Survey (CPS) and American Community Survey (ACS) are exceptions, as they summarize the labor force based on residency, i.e. where people live. The Census Bureau datasets tend to be more geographically detailed and present data at one point in time, while the BLS datasets tend to be more timely and are focused on providing data in time series. The BLS gives you the option to look at employment data that is seasonally adjusted; this data has been statistically “smoothed” to remove fluctuations in employment due to normal cyclical patterns in the economy related to summer and winter holidays, the start and end of school years, and general weather patterns.

Many of the datasets are subject to data suppression or non-disclosure to protect the confidentiality of businesses; if a given geography or industrial category has few establishments, or if a small number of establishments constitutes an overwhelmingly majority of employees or wages, data is either generalized or withheld. Most of these datasets exclude agricultural workers, government employees, and individuals who are self-employed. Data for these industries and workers is available through the USDA’s Census of Agriculture and the CB’s Census of Governments and Nonemployer Statistics.

The CB datasets are published on the Census Bureau’s website via the American Factfinder, the new data.census.gov, the FTP site and API, and via individual pages dedicated to specific programs. The BLS datasets are accessible through a variety of  applications via the BLS Data Tools. For each of the datasets discussed below I link to their program page, so you can see fuller descriptions of how the data is collected and what’s included.

The Census Bureau’s Business Data

Business Patterns (BP)
Typically referred to as the County and ZIP Code Business Patterns, this Census Bureau dataset is also published for states, metropolitan areas, and Congressional Districts. Published on an annual basis from administrative records, the number of employees, establishments, and wages (annual and first quarter) is published by NAICS, along with a summary of business establishments by employee size categories.
Economic Census
Released every five years in years ending in 2 and 7, this dataset is less timely than the BP but includes more variables: in addition to employment, establishments, and wages data is published on production and sales for various industries, and is summarized both geographically and in subject series that cover the entire industry. The Economic Census employs a mix of enumerations (100% counts) and sample surveying. It’s available for the same geographies as the BP with two exceptions: data isn’t published for Congressional Districts but is available for cities and towns.

Bureau of Labor Statistics Data

Current Employment Statistics (CES)
This is a monthly sample survey of approximately 150k businesses and government agencies that represent over 650k physical locations. It measures the number of workers, hours worked, and average hourly wages. Data is published for broad industrial categories for states and metropolitan areas.
Quarterly Census of Employment and Wages (QCEW)
An actual count of business establishments that’s conducted four times a year, it captures the same data that’s in the CES but also includes the number of establishments, total wages, and average annual pay (wages and salaries). Data is tabulated for states, metropolitan areas, and counties at detailed NAICS levels.
Occupational Employment Statistics (OES)
A bi-annual survey of 200k business establishments that measures the number of employees by occupation as opposed to industry (the specific job people do rather than the overall focus of the business). Data on the number of workers and wages is published for over 800 occupations for states and metro areas using the Standard Occupational Classification (SOC) system.

Labor Force Data by Residency

Current Population Survey (CPS)
Conducted jointly by the CB and BLS, this monthly survey of 60k households captures a broad range of demographic and socio-economic information about the population, but was specifically designed for measuring employment, unemployment, and labor force participation. Since it’s a survey of households it measures the labor force based on where people live and is able to capture people who are not working (which is something a survey of business establishments can’t achieve). Monthly data is only published for the nation, but sample microdata is available for researchers who want to create their own tabulations.
Local Area Unemployment Statistics (LAUS)
This dataset is generated using a series of statistical models to provide the employment and unemployment data published in the CPS for states, metro areas, counties, cities and towns. Over 7,000 different areas are included.
American Community Survey (ACS)
A rolling sample survey of 3.5 million addresses, this dataset is published annually as 1-year and 5-year period estimates. This is the Census Bureau’s primary program for collecting detailed socio-economic characteristics of the population on an on-going basis and includes labor force status and occupation. Data is published for all large geographies and small ones including census tracts, ZCTAs, and PUMAs. Each estimate is published with a margin of error at a 90% confidence interval. Labor force data from the ACS is best used when you’re OK with generally characterizing an area rather than getting a precise and timely measurement, or when you’re working with an array of ACS variables and want labor force data generated from the same source using the same methodology.

Wrap Up

In the book I’ll spend a good deal of time navigating the NAICS codes, explaining the impact of data suppression and how to cope with it, and covering the basics of using this data from an economic geography approach. I’ve written some exercises where we calculate location quotients for advanced industries and aggregate ZIP-Code based Business Patterns data to the ZCTA-level. This is still a draft, so we’ll have to wait and see what stays and goes.

In the meantime, if you’re looking for summaries of additional data sources in any and every field I highly recommend Julia Bauder’s excellent Reference Guide to Data Sources. Even though it was published back in 2014 I find that the descriptions and links are still spot on – it primarily covers public and free US federal and international government sources.

BLS Data Portal

Bureau of Labor Statistics Data Tools

Sedona Hike

XYZ Tiles and WMS Layers in QGIS 3

I did a lot of hiking around Sedona, Arizona a few weeks ago, and wanted to map my GPS way points and tracks in QGIS over some WMS (web mapping service) base map layers. I recently switched to QGIS 3 since I need to use that in my book (by the time it comes out 2.18 will be old news), and had to spend time starting from scratch since the plugin I always used was no longer available (ahhh the pitfalls of relying on 3rd party plugins – see my last post on SQLite). I thought I’d share what I learned here.

I was using the OpenLayers plugin in QGIS 2.x as an easy resource to add base maps to my projects. You could pull in layers from OSM, Google, Bing, and others. It turns out that plugin is no longer available for QGIS 3.x. So I searched around and found some suggestions for a different plugin called QuickMapServices which was a great replacement. But alas, that worked in QGIS 3.0 but is not compatible (as of now) for QGIS 3.2.

So I’m back to adding WMS layers manually. There is a new feature in QGIS for adding XYZ Tiles; this is a little better than WMS because the base map can be rendered a bit quicker. I found a tip in the Stack Exchange that you can add an OSM tiles layer with this url:

http://tile.openstreetmap.org/{z}/{x}/{y}.png 

Select XYZ Tiles in the Browser, right click, New connection, give it a name, add the URL. You can modify the X Y Z coordinates where the map centers and zooms by default. Once you’ve created the connection, you can simply drag the OSM layer into the map window to render it.

Adding the OSM XYZ Tiles in QGIS

One problem that always creeps up: when you add other layers and adjust the zoom, sometimes the rendering of the base map looks poor, i.e. the features and labels look blurry or blocky. When you’re pulling data from a web map layer, as you zoom in it swaps out the tiles for more detailed ones appropriate for that scale. But when you’re zooming in QGIS things can get out of synch, as your map window zoom may not be enough to trigger the switch in the map tiles, or those map tiles are just not meant to be rendered at that scale. If you right click on a blank area of the toolbar, you can activate the Tile Scale panel and can use the slider to adjust the window zoom in synch with the tiles, so you can operate at the scales that are appropriate for the tiles. The way points and track for our hike alongside Schnebly Hill Road are shown below, and the labels for the points represent our elevation in feet.

OSM Tile Layer with Tile Scale Panel

If the slider is grayed out, select the OSM layer in the Layers menu, right click, and select Set CRS  – Set Project CRS From Layer. Web mapping services typically use EPSG 3857 Pseudo Mercator as the coordinate reference system / map projection by default. If your other vectors layers aren’t in that system, you can have the base map draw to their system or vice versa by selecting the layer, right clicking, and choosing Set CRS. But for the tile scale to work properly EPSG 3857 must be the project CRS.

Lastly, I’ve always liked the USGS WMS layers, which are never included in the plugins that I’ve seen. The USGS provides layers for: imagery, imagery with topographic features, shaded relief, and the USGS topographic map layer:

https://basemap.nationalmap.gov/arcgis/rest/services

USGS Link for WMS Layer for Topographic Maps

You can click on one of the services, and at the top (in small print) are urls for their services in WMS and WMTS. The last one is a web mapping tile service, which is a bit faster than WMS. Click on the WMTS link, and copy the url from the address in the browser. Then in QGIS select WMS / WMTS layers, right click, add a new connection, give it a name and paste the url. This is url for the topographic map:

https://basemap.nationalmap.gov/arcgis/rest/services/USGSTopo/MapServer/WMTS/1.0.0/WMTSCapabilities.xml

Once again, you can drag the layer into the map window to render it, and you can use the Tile Scale panel to adjust the zoom. Here’s our hike with the topo map as the base:

USGS Topographic Map in QGIS

SQLite Logo

Looking for a Good SQLite GUI?

Goodbye SQLite Manager…

Late last year, I discovered that my favorite SQLite GUI was defunct. The SQLite Manager was a plugin for Firefox that allowed you to create and interact with SQLite databases with a simple yet highly functional interface. It had good support for importing and exporting csv files, color coding of cells based on data types, and a convenient feature for cycling back and forth between your SQL statements. Since it was a Firefox plugin it was guaranteed to work on any operating system, and since Firefox is installed on machines across my campus I knew I could rely on it for creating data extracts for students and faculty – I’d package data up in SQLite and send it to them along with a link to the plugin.

Firefox goes through about a million versions a year these days, and after a major upgrade last fall (to Firefox Quantum) most of the existing plugins, including the SQLite Manager, were no longer compatible. An upgrade it highly unlikely, as a few things changed under the hood of Firefox that makes the plugin unusable. While it still works on the Firefox Extended Support Release, in the long run the writing is on the wall.

Hello DB Browser for SQLite!

After searching through many alternatives I discovered the DB Browser for SQLite. It runs on Windows, Mac, and Linux and there’s a version for mobile. It was easy to install and has a clean interface. It provides a number of convenient tools and menus that you can use in place of writing SQL DDL, and in some cases it expands the functionality of SQLite by enabling a number of ALTER TABLE commands that are not part of SQLite SQL (like renaming and dropping columns). The Browse Data window makes it easy to quickly thumb through, sort, and filter records and to edit individual values by hand. The Execute SQL window has auto-complete and color-coded syntax, and you can see the database schema in one tab as you write your SQL in another (making it easy to reference table and column names). You can import and export data as CSV (or any delimited text file) or SQL files, and you can save the results of SELECT queries as CSV.

One interesting addition is that there’s actually a Save (Write Changes) and Undo button. So when you create, modify, or drop records, columns, or tables you see the result, but the act isn’t final until you commit the changes. A nice safety feature, especially for db novices.

DB Browser for SQLite - Browse

Browse Data

DB Browser for SQLite - SQL

Execute SQL and View DB Schema

I encountered a few quirks, but nothing insurmountable. I was using the nightly build version without realizing it, and when importing a CSV file the database takes a best guess as to what the data types for the columns should be. Even though the import screen gives you the option to specify that values are quoted, my quoted numeric fields were still saved as numbers and not text. As a result, ID codes like FIPS or ZIP Codes lose their leading zeros and are saved as integers.

The project is managed on github, so I went ahead and posted an issue. The developers were super responsive, and a discussion ensued over whether this behavior was desirable or not. We found two work-arounds. First, if you build an empty table with the desired structure, and then go to import the CSV, if you provide the name of that empty table as the new table name the db will import your data into that table. Alternatively, if I went and downloaded the latest stable release (3.10.1) the default behavior is that all columns are imported as text, which is a safer bet. You can use the GUI to change the types after import. The issue was marked as a bug, and will be addressed in a future release – one possible solution is to provide an option to turn the autodetect feature on (to determine what the types should be) or off (to import everything as text).

The browser also has a feature to attach a database to the current database, but when you do the attachment it appears like nothing happened – you can’t see  or browse the objects in database number two. But it IS attached (you can see every statement that’s been executed in a helpful log window) and you can copy a table from one db to the other like this:

CREATE TABLE sometable AS
SELECT *
FROM database2.sometable;

You run this within the current database, and database2 is the attached database (when you attach a db you provide an alias for referencing it).

These are minor quibbles. The DB Browser for SQLite is cross-platform, stable, has a clean interface with nice features, and is actively developed by a responsive and friendly team. I’ll be using it for all my SQLite tasks and projects, and will recommend it to others.

Spatialite?

An alternative I considered was to simply use the Spatialite GUI for both regular and spatial databases. It also has a simple, solid, and functional interface and supports spatial SQL, giving you the best of both worlds. So why not? While it works great for my own purposes it’s not something I can recommend to new users who are not GIS folks, either in my work or in the census data book I’m writing. Just figuring out where to download it from the website is overly complex, and while there are binaries for MS Windows there are none for Mac users. You’d have to install it from the source files, which is over the top for novices. Linux users may get lucky and find it in their software repos (it’s included for Debian and Ubuntu). The database browser in QGIS has matured in recent years, so that’s another option for GIS users who want to work with Spatialite or PostGIS.

Now if we only had a good GUI for PostgreSQL… I tried pgAdmin 4 about a year ago, and it was so bad that I’m still clinging to pgAdmin III as long as it still lives. But this is a different story, and one I’ll return to and investigate fully when it comes time to teach my spatial database course next year.

Census 2020

Upcoming Changes in the 2020 Census

As I’m four chapters into my writing my book on the census, I’m paying close attention to what’s going to happen in 2020. Now that the Census Bureau has outlined the subjects and the specific wording of the questions for the 2020 census and future ACS, here’s my summary of what’s changing, and what’s not. NPR has been doing an excellent job covering every aspect of this, and I link to their articles throughout this post.

What Changes

Residency for the military. Up until now, members of the military who were temporarily deployed overseas were counted at their US address at their time of enlistment. Many communities that are home to military bases have argued that this severely impacts their counts, and that the census should count personnel based on where their home military base is (remember, in addition to apportioning Congress, $600 billion in federal aid is distributed annually to state and local governments based on census numbers). The Census Bureau agreed, and this change will be implemented in 2020.

Same sex marriage. The question on relationships will change, so that people can explicitly state whether they have a married or unmarried partner, and whether their spouse or partner is opposite sex or same sex. Up until now, the Census Bureau recoded every household that indicated that they were married people of the same sex to unmarried partner status, and did a special tabulation using the relationship and gender questions to count same sex partnerships. Now that same sex marriage is recognized under federal law (as of 2015), the ambiguity of counting people by relationship can be cleared.

Ethnicities for white and black. For the first time, white and black people will be able to write in a specific nationality or ethnicity under their race, just as people of other races (Asian, Native American, Hawaiian or Pacific Islander) and of Hispanic and Latino ethnicity have long been able to do.

Citizenship. This is a pretty controversial one that may change by the time we get to 2020, but due to Justice Department lobbying and pressure from the Executive branch, there will be a question that asks whether a person is a US citizen or not. Ostensibly they say this data is necessary to combat voter fraud, although there is absolutely no evidence that widespread voter fraud exists. I’ll address this in more detail at the end of this post.

What Stays the Same

Residency for people in prison. Parallel to the military discussion, many have argued that people who are incarcerated should be counted at their home address, and not where they are incarcerated. This practice has an adverse impact on communities that suffer from crime and need political representation and funding, while artificially inflating the populations of rural communities where large prisons are typically located. The Census Bureau disagreed, maintaining that prison is the usual place of residence for people who are incarcerated, and therefore they should be counted there.

Measuring gender. The census asks people to identify their sex as male or female, and specifically uses the term sex instead of gender to emphasize that they are asking about simple biology and chromosomes, and not about sexual identification, expression, or preference. Many (including several federal agencies and members of Congress) have lobbied for a question on gender identification and sexual preferences, arguing that it is necessary under civil rights legislation. No such questions will be added.

Hispanic and Latino and race. The Census Bureau conducted almost a decade of studies and tests on revising the race and ethnicity questions, and the biggest suggestion was to make Hispanic and Latino a race and not a separate ethnicity. Their studies showed that society, and people of Hispanic ethnicity, largely view Hispanic and Latino as a race. A large percentage of Hispanics choose Other as their race, since Hispanic is not an option under the race question. The Office of Management and Budget chose to simply ignore these suggestions, and since approval from OMB is necessary (as data on race is collected across the federal government) the questions will remain the same. The second major suggestion, also ignored, was to create a new racial category for Middle Eastern and North African (MENA) people who are currently counted as white. With the exception of the optional ethnic write-ins for whites and blacks, the racial and ethnic categories for 2020 will be the same ones used in 2000 and 2010.

The Citizenship Question Controversy

Is asking about citizenship really new? The oft-quoted “fact” in the media is that this question hasn’t been asked since the 1950 census, but that’s only partially true. This could be the first time since 1950 that this question was asked as part of the short census form that 100% of the population is asked to complete. The short form (from 1960 to 2000) and the only form (2010 to present) is designed to record just the basic demographic characteristics of the population for the purpose of reapportioning seats in Congress, redrawing legislative boundaries, and providing fundamental baseline numbers on which other statistical products are based. Citizenship had previously been asked on the sample long form (from 1960 to 2000 sent, to 1 in 6 households) and is currently asked on the American Community Survey (2005 to present, sent to 3.5 million addresses annually) The long form and ACS were designed for a different purpose: to measure the broad socio-economic characteristics of the country. Both the ten-year census and the ACS ask questions which are required under federal law, to meet different legislative obligations.

Why is this controversial? Not only is the citizenship question unnecessary for fulfilling the basic requirements of the decennial census, it’s actually detrimental. The Census Bureau is charged with counting every single person in the United States, regardless of their voting eligibility, citizenship status, or legality. As long as a person isn’t a visitor or tourist, they are counted as a resident. None of these characteristics (with the sole exception of counting slaves as three-fifths of a person prior to the Civil War) has ever been a factor in whether a person is counted in the census or not. Congressional seats have always been apportioned based on total population, and legislative districts have long been delineated using total counts.  This was the intent of the Founding Fathers and has been upheld by the Supreme Court numerous times (as recently as 2016).

Given the fear that many immigrants have of government officials, especially from this current administration which has shown unbridled hostility, it’s likely that many non-citizens (legal residents and undocumented alike) will not fill out the census form, thus resulting in an undercount and a possible loss of political power and federal aid for states that have high immigrant populations (which tend to be blue states, but not exclusively so). In order for the Census Bureau to insure confidence in the counting process, and to get the most accurate count (which is their primary mission), they have to assure people that their personal, individual data will not be published or shared, and they must make it clear that they have no relationship with regulatory branches of the government that would use this data against them.

But even though individual responses are kept confidential for 72 years (the Census only publishes data summarized by population groups and geography), data from the ten year census is available at the census block level, which is the smallest and finest level of geographic detail. This data could potentially be used (by the Border Patrol or ICE) to identify and target small clusters of areas that have a high percentage of non-citizens. If you think this sounds far-fetched, see this article from the Washington Post: it’s essentially what happened to Japanese American citizens who were rounded up for internment during WWII.

Obviously, it’s not in the Census Bureau’s best interest to add this question. The announcement that it was being added was made by the head of the Commerce Department, of which the Census Bureau is part, and not by the Bureau itself. The Census Bureau meticulously studies and tests every question that gets added to the form years in advance, and it seems clear that this was something that was tacked on at the last minute.

Congress could move to strip this question from the form; it’s pretty unlikely this will happen now, but it could if the midterm elections flip the legislature to the Democrats. There are also a number of lawsuits from different states to try and stop the question from being added. Whether the question stays or goes, I think the damage is done and the count will be negatively impacted, given the sad state of our government. If the question remains it could end up being a useless data point, as people may still fill out the form and skip that question. While you’re required by law to complete the census, it seems unlikely that the Census Bureau will be able to chase after millions of people who refuse to answer a single question.