Nearly half of the District’s children under five are enrolled in D.C.’s Books From Birth program

The District of Columbia Public Library (DCPL) Books From Birth program mails all enrolled children in D.C. a free book each month from birth until they turn five. The program was launched by DCPL in January 2016 in partnership with Dolly Parton’s Imagination Library. The program just celebrated its one year anniversary, and we thought it would be interesting to see how the program is performing now that participation data is available.

In its first thirteen months, the Books From Birth program enrolled nearly 22,000 unique children and mailed 147,525 books. The 2015 American Community Survey estimates that approximately 40,400 children under the age of five live in the District. This translates to a 47 percent participation rate for the program – nearly half of D.C.’s under five-year-old population. We were curious to see how D.C.’s first year participation rates compare to other jurisdictions with similar programs

Shelby County, Tennessee, which includes Memphis, is an urban area that has been operating a program like D.C.’s since 2005. Shelby County has a population of 937,750 (657,167 residing in Memphis) and generally speaking has similar demographics to the District.

Shelby County

District of Columbia

Population

937,750

647,484

Under Five Population

67,000

40,400

Percent high school graduate or higher 86.9%

89.3%

Black

53%

50%

White

40%

42%

Asian

3%

5%

Latino

6%

10%

Percent Living in Poverty

21%

18%

Source: U.S. Census Bureau, 2011-2015 American Community Survey 5-Year Estimates

We plotted the first thirteen months of enrollment and participation data for the Shelby County and D.C. programs to see how they compare. The following graphs show the results of this plot. (click to enlarge)

BFB Participation Percentage

BFB Participation Number

The data shows that D.C.’s Books From Birth program outpaced Shelby County participation by about 300 percent and had more than double the number of enrolled children at the conclusion of month thirteen. The District enrolled more children in total even though Shelby County has 67,000 children under five years old compared to the District’s 40,400. We speculate that D.C.’s higher enrollment figures could be related to the fact that DCPL implemented an aggressive promotional campaign. DCPL’s campaign included posters on public transit and outreach at neighborhood festivals, DCPS parent meetings, nonprofit and government agencies, and daycare providers. Shelby County did not ramp-up its promotional outreach efforts until several years into the program and did not simplify its enrollment application until 2011. Shelby County saw swift growth in enrollment once outreach efforts were expanded. The program currently has 44,250 program participants and a 66 percent participation rate.

We also looked at where D.C.’s program participants live by using the zip code of each child’s mailing address. The top three enrolling zip codes were 20011 (Brightwood Park, Crestwood, Petworth), 20019 (Deanwood, Burrville, Lincoln Heights, River Terrace, Benning Ridge), and 20002 (Capitol Hill, NoMa, Trinidad, Kingman Park). (Click map to interact)

Enollment by Zip

The top three highest zip codes for participation rate (number of children enrolled out of the total number of eligible to enroll) were 20024 (Southwest Waterfront, Navy Yard), 20002 (Capitol Hill, NoMa, Trinidad, Kingman Park), and 20012 (Takoma, Shepherd Park, Colonial Village). (Click map to interact)

Participation by Zip

We also separated children into five buckets based on birth year to look at the age of participants by zip code. We found that the largest age cohort among Books From Birth children is newborns (under the age of one) and the smallest cohort is four-year-old children. All zip codes generally follow the same age cohort patterns except for 20018 (Woodridge, Langdon, Fort Lincoln) which had more four-year-old participants than newborns. (click to enlarge)

BFB Age by Zip

What exactly is this data?

Our data on Books From Birth participants comes from the data reported to us by the District of Columbia Public Library. This included the birth years for all participants, zip codes for mailing address, and enrollment numbers for each month of the program. We excluded zip codes with under 50 participants since many were not a physical location but rather a zip code for P.O. boxes. Excluded zip codes are included in the total enrollment and participation numbers but not the participation by location analysis.

The data regarding Shelby County was provided by the Executive Director of the Shelby County Books From Birth, Jamila Wicks.

Our data on the number of eligible children by zip code and demographics for Shelby County and D.C. comes from the 2011-2015 American Community Survey five-year estimates for number of children under five years old by zip code.

Are you related to someone named Michael or Mary?

So are many of District residents.  Michael and Mary happen to be two of the most popular names for men and women in the District of Columbia. In fact over 3,000 Michaels and 1,700 Marys submitted income tax filings in 2014, over 6,000 Michaels and 3,600 Marys are registered to vote, and over 29,000 Michaels and 23,000 Marys have been born in D.C. since 1910.

After reading an article about first names in the Washington Post and after speculating about what our two colleagues would name their now one-month-old babies, we decided to take a closer look at the frequency and characteristics of first names in D.C.

In order to conduct this analysis, we used three separate databases:

  • 2013 income tax filings (first names of single and joint filers);
  • Voter Registration Data (first names, party affiliation, ward, and zip code of home address); and,
  • Social Security Records (first names of individuals born in D.C. with a Social Security card since 1910).

We then filtered and ranked the data and limited our analysis to top 500 most popular names. Check out the interactive table below to see if your name made the top 500.

(click to interact)

Name Frequency Among District Residents

 Name frequency since 1910

In addition to ranking names by popularity, we used Social Security data to plot the frequency of names in every birth year since 1910. As you scroll through each year you can see how a name’s popularity changes over time for all births in the District. A note of caution – data in early years is likely not as accurate as the most recent. Nonetheless, it is interesting to see how naming fads come and go with time.

(click to interact)

First Name by Birth Year (all names)

The interactive graph below visualizes how specific names change in popularity over time. For example, we again chose Michael and Mary. If you are related to a Michael born in the District, there is a good chance that he was born before the 1990s; and if you are related to a Mary born in the District, she was likely born before the 1970s. Both names were wildly popular over the past century but both have declined in frequency – Michael peaked in 1958 and Mary in 1946.

(click to interact)

First Name by Birth Year (individual name)

First Name Voter Registration Data

To analyze the relationship political party affiliation has with first names, we calculated the frequency of first names among active registered voters and their political parties. The interactive map below shows party affiliation by name and zip code.

(click to interact)

Voter Registration by First Name

The sortable list below can be used to compare the top 500 most frequent names among registered voters and the political parties they most frequently register with.

(click to interact)Voter Registration by First Name Sortable List

Here are the most and least common names by political party affiliation.

Party Highest Lowest
Democrats Lilly Brendan
Libertarian Jared Barbara
Independent Jose Laverne
Other Gabriel Laverne
Republican Tyler Beatrice
Statehood Green Jon Laura

We also created a tool to analyze political party affiliation by name and Ward.

(click to interact)

Voter Registration by First Name and Ward

Enjoy playing around with the interactive tables and graphs and let us know if you have any interesting observations.

 

 

 

 

12 years of change in D.C. economy, yet some residents remain excluded from employment booms

A recent blog post noted that in the five years after the Great Recession, DC resident employment increased rapidly, but unemployment went down only slowly.

The loose connection in D.C. between resident employment and unemployment is not new, but remains cause for concern. From 2009 to 2014, DC unemployment went down by only one for every increase of ten in employed residents.

image007

In the 2002 to 2007 pre-recession period, however, the comparable impact of resident employment gain on unemployment was even less. Then the decline was only one-half of an unemployed person for every ten additional employed residents (that is, reduction in the number of unemployed DC residents, 1,041, was only 5 percent as much as the 23,652 increase in resident employment). The strong growth in labor force and resident employment relative to the population overpowers the relationship between unemployment and resident employment in the chart below:

image008

The relationship between resident employment and unemployment in D.C. was also quite loose in the recession years. From 2007 to 2009, unemployment rose by 15,829, but there was only a decline of 1,920 in employed residents. That is, unemployment rose 8 times faster than the resident employment. In the U.S. the relation was closer to one-to-one, and in the suburbs, less than 1 to 2.image005

Situations in which an increase in employed residents is associated with a relatively small decrease in the number of unemployed are not unique to D.C., but they seem to occur where job growth occurs and the unemployment rate is already quite low.  For example, in the U.S. from 2002 to 2007, the decrease in the number of unemployed was about 10 percent of the growth in resident employment, but the U.S. was starting from a 5.6 percent unemployment rate, close to what some thought at the time was a full employment number. In the DC suburbs, over the 2002 to 2007 period, the reduction in unemployment was about 7 percent of the growth in employed residents, but the suburban unemployment rate in 2002 was 3.4 percent. In North Dakota, as the energy sector expanded from 2009 to 2014, the decline in the number of unemployed was 5 percent of the gain in resident employment, but in 2009 the unemployment rate there was 3.6 percent. These are all instances where the booming economy and a tight labor market invites new working adults to join the labor force.

In D.C.’s case, the unemployment rate in 2009 was 10.0 percent and the bump up in resident employment over the next five years did not translate into more jobs for the existing unemployed.  Even when plenty of people were unemployed in the District, they did not benefit from the economic expansion or found jobs.

The BLS labor force and Census population numbers are summary data snapshots at two points in time. These snapshots, as important as they are, do not give a very good picture of the degree to which changes may be occurring in the composition of the labor force and population that have a bearing on the connection between resident employment and unemployment. But these snapshots do suggest some significant changes have occurred in the dynamics of the DC market over the past 12 years.

This can be seen most clearly in the pre-recession period from 2002 to 2007. DC’s labor force went up by 22,611 over those years while the population increased by only 2,575. Where did the additional labor force come from? Some no doubt came from the existing population as the falling unemployment rate encouraged more people to seek and obtain work. But to get such a large increase from the existing population seems questionable when the 1,041 decrease in unemployment that occurred was such a small percentage of the growth in resident jobs. The most likely explanation is that the growth in the labor force was largely the result of migration patterns, the churn in the city’s population. With little change in the overall level of population, the net result of who moved in and who moved out could easily have produced an increase in persons who were in the labor force and employed.

image011

Summary labor market and population statistics do not give information on who the unemployed are and how the composition of unemployment may have changed over the last 12 years, but such details are important for understanding current labor market dynamics in D.C. and when considering policy remedies. From numerous studies that have been done in D.C. and elsewhere it would be expected that a large share of the unemployed in D.C. are people lacking job skills and work experience. Beyond that, however, other matters to consider are the degree of turnover among the unemployed, how important the discouraged worker phenomenon is in the composition of measured unemployment in D.C., whether migration patterns that have increased the labor force also contribute in some way to D.C. unemployment, how long unemployed persons have lived in D.C., and whether the operation of the District’s unemployment insurance system has any particular effect on measured unemployment. It would also be useful to know more about how the relationship between resident employment and unemployment described here for DC compares with that of other cities.

What exactly is this data? Labor market data is from the Bureau of Labor Statistics. BLS develops these statistics with the assistance of models, with inputs that include population numbers from Census, unemployment insurance statistics, the American Community Survey, and the Current Population Survey administered by the Department of Labor for and in cooperation with Census. BLS revises data as more information becomes available. The data used here reflect the comprehensive revisions released on March 4 2015 to take account of population and other changes; these revisions were not just to the most recent year but to the entire data series.

Labor market data is the seasonally unadjusted quarterly average for the December quarter for the years shown. The data for the DC suburbs is calculated by subtracting amounts for the District of Columbia from the totals for the Washington Metropolitan Area.

Population numbers for the December quarter are taken from Moody’s Analytics (Economy.com), which derives quarterly estimates from annual Census Bureau population numbers.

DC labor market over the last 5 years: many more jobs for residents, modest cut in unemployment

According to the US Bureau of Labor Statistics, from December 2009 to December 2014, the period of recovery from the Great Recession and subsequent expansion, the number of employed D.C. residents increased by 49,166—that is 16.1 percent. The percentage increase is quite remarkable—two-and-a-half times the rate across the US as a whole (6.4 percent) and the D.C. suburbs (6.5 percent), and even greater than in the resident employment growth in North Dakota (15.6 percent), an energy boom state, with many new jobs attracting a lot of new residents.image002What accounts for the increase in resident employment? It is not reduction in unemployment. From 2009 to 2014, D.C. resident employment grew by 49,166 while unemployment declined by only 4,776. This is a modest decline: For every 10 additional D.C. residents who got jobs since 2009, the number of unemployed residents went down by only one. The relatively loose relationship between resident employment and unemployment contrasts with the suburbs and the entire US, where for every gain of 10 in employed residents, the declines in unemployment were an average of 2.6 and 6.8, respectively.

Here is another way to look at it: Over the past five years, the District reversed only about third of the increase in unemployment caused by the recession. The suburbs reversed nearly half of it and the US reversed nearly 80 percent.

image004The most likely explanation for why resident employment rose so rapidly and unemployment fell so slowly is the growth of DC population. From 2009 to 2014, DC’s population increased by 65,400. This 10.9 percent increase in population growth did not directly cause either the 13.1 percent increase in DC’s labor force or the 16.1 percent increase in DC’s resident employment.

image006

Some of the increase may well have come from the existing population reentering the labor force; however, these two labor market indicators could not have grown as they did in the absence of more population. The opportunities for employment in both DC and the suburbs (reachable from DC by commute) appear to have attracted additional people to the city. The March trend report coming out this week has much more on this topic.

We will write more on the relationship between population, resident employment, or unemployment in another post.

What exactly is this data? Labor market data is from the Bureau of Labor Statistics. BLS develops these statistics with the assistance of models, with inputs that include population numbers from Census, unemployment insurance statistics, the American Community Survey, and the Current Population Survey administered by the Department of Labor for and in cooperation with Census. BLS revises data as more information becomes available. The data used here reflect the comprehensive revisions released on March 4, 2015 to take account of population and other changes; these revisions were not just to the most recent year, but also to the entire data series.

Labor market data is the seasonally unadjusted quarterly average for the December quarter for the years shown. The data for the DC suburbs is calculated by subtracting amounts for the District of Columbia from the totals for the Washington Metropolitan Area.

Over past decade, share of low-income households has decreased in neighborhoods close to downtown

The District is an expensive place to live. The Economic Policy Institute estimates that most families with children need to spend at least $80,000 a year to have an “adequate but modest” life in the D.C. metro area. Housing is notoriously expensive too, as we pointed out in our recent post on growing property assessments.

In a city as expensive as D.C., where do low-income people live? And how has this changed over the past decade, as D.C. saw a lot of new construction, much of it residential?

To answer these questions we looked to see where families making under $40,000, and childless singles making under $20,000, lived in 2002 and 2013 according to our local income tax records. We defined a family as any household with two or more people. The income figures (the $40,000 and $20,000) are in 2014 dollars.

The map below shows a refrain heard throughout the city: the District is divided by Rock Creek Park and the Anacostia River. East of the river more than half of all tax-filing households are low-income (as we define it). In most neighborhoods west of the park that figure is closer to 10 percent.

Perhaps the more interesting story, though, is in the middle of the city–the area between the river and the park south of Petworth, Brookland, and Woodridge. Here, since 2002, the shares of low-income households have decreased, in some cases quite drastically. In zip code 20001 (Shaw, Bloomingdale, Mt. Vernon Triangle), the percentage of households making under $40,000 (or $20,000 for singles) dropped by 11 percentage-points, from 47 percent to 30 percent. In zip code 20002 (Eckington, NoMa, Trinidad, H Street), the share of low-income filers dropped 11 percentage points. (For a great zip code primer, check out this map from NeighborhoodInfo DC.)

low income

But remember these are shares, and not levels. Is there an exodus of low income people from the city? In all zip codes but one (20036, which is downtown, south of Dupont), the number of low-income filers increased between 2002 and 2013. The shares decreased because the number of filers making more than $40,000 increased much more.

The tax data tells us that low-income singles still stay in the city whereas families don’t. The number of low-income filers increased in almost all zip codes because the number of childless singles making below $20,000 increased almost everywhere (see the bottom, right map). Our guess is that at least some of these low-income singles without kids are students and recent college graduates who have high earning potential but currently don’t make much money.

Families making under $40,000, though (bottom, left map), disappeared from many neighborhoods, especially those in zip code 20002 (Eckington, NoMa, Trinidad, H Street) and 20003 (Capitol Hill, Navy Yard). They increased in the north-central swath of the city between Columbia Heights and the District’s northern tip, which includes neighborhoods like Petworth and Brightwood Park.

lowincome breakdown rawWe suspect these maps will put numbers behind the changes District residents have been seeing in their neighborhoods over the past 10-plus years.

What exactly is this data? We analyzed D.C.’s local income tax data from tax years 2001 and 2012 (filed by residents in calendar years 2002 and 2013). “Income” is federal adjusted gross income and all dollar amounts we reference are in 2014 dollars. When we refer to “families” we mean households that have two or more people, according to tax records. “Childless singles” or “singles without kids” means people who file as unmarried and claim no dependents. We excluded from our analysis people who filed for only a partial year; dependents who filed taxes; and filers with federal adjusted gross incomes less than zero. We also excluded people who filed as married on separate returns because we could not calculate household incomes for this group. These filers make up 3 percent of all local income tax filers in D.C. For tax year 2012 we have valid D.C. zip codes for 93 percent of filers and for tax year 2001 we have valid D.C. zip codes for 67 percent of filers. Our estimate of the change in number of filers by zip code assumes that the zip codes we have in our data are representative of the zip codes of all filers.

How D.C.’s neighborhoods have changed since 2002

Last week we looked at the types of families living in different neighborhoods in 2013. Today we see how the mix of families in neighborhoods has changed since 2002.

The maps below show that in neighborhoods east of Rock Creek Park, childless singles became a larger share of tax filers while singles with dependents shrank compared to other family types. The biggest changes happened in neighborhoods close to the eastern end of downtown, like Shaw, Bloomingdale, Chinatown, NoMa, and H Street. In these neighborhoods the share of childless singles rose by double-digits and the portion of households consisting of singles with dependents decreased by double-digits.

We see a different story in neighborhoods west of Rock Creek Park. There, the share of taxpayers who were childless singles remained about the same, and even decreased in the far western corner of the District (which includes neighborhoods like AU Park, Spring Valley, Tenleytown, and Cathedral Heights). Meanwhile, there were no significant changes in the share of singles with dependents west of the park.

childless singles changesingles w dep change

How do married couples fit into this picture? People who were married with no dependents became a larger share of households in neighborhoods close to downtown, like Adams Morgan, U Street, Shaw, Logan Circle, and H Street. Married people with dependents became a larger share of households in neighborhoods adjacent to the eastern end of downtown–like Capitol Hill and H Street–as well as farther-out neighborhoods in Northwest, like Foxhall, Palisades, and Spring Valley.

Some people might be surprised to see that neighborhoods like Petworth saw virtually no change in the share of taxpayers who were married with dependents. In fact, the number of married people with dependents around Petworth (zip 20011) did increase, but the share stayed about the same since so many more childless singles moved into the area.married no dep change

married with dep change

This is a lot of information to digest. To get a better sense of neighborhood-by-neighborhood changes, we created the tool below. It shows you the mix of families in different D.C. zip codes in 2002 and 2013. Click on the graph below to access the tool.

Click here or on the graph below to see changes by neighborhood

mix of families tool
What exactly is this data? We used data on people filing local income taxes in D.C. in 2001 and 2012. The addresses listed on this data will typically reflect where people lived in 2002 and 2013. “Childless singles” are people who filed as single and claimed no dependents. “Married with dependents” refers to people who filed as married on the same return and claimed dependents. “Married with no dependents” refers to people who filed as married on the same return and clamied no dependents. “Singles with dependents” are people who filed as heads of household and claimed dependents. We excluded all other types of filers (domestic partners, dependents filing taxes, people with dependents who file as single instead of head of household) from our analysis.

Where is the vacant land in the District?

Along with news on population growth and increasing housing prices, we often hear the concern that the small footprint of the city is an impediment to growth.  The discussions on height limitations in the city, work done by the Urban Institute and by our good friends at the Office of Planning made us think: How much vacant land is there in the District that can one day be developed?

We searched the District’s real property tax database for empty, unbuilt land.  Of course, not all of this land is immediately buildable.  Zoning and ownership factors greatly affect allowable development on these lands.  And some of this land might never be buildable (Parks, rail tracks, other bits of land–do we want to see buildings in the Arboretum?).  So think of this as an exercise over 300 years where population growth might make our current preferences less relevant.

Here is what we found:

image002

  •  The District has approximately 300 million square feet of vacant, unimproved land. This accounts for about 19 percent of all land in the District’s real property tax database.
  • Of this vacant land, only 14 percent (42 million square feet) is in the hands of private entities and individuals. These would be the simplest to build on (barring zoning limitations).  Privately owned lots are concentrated in zip codes 20017 through 20020 and in 20002.
  • Non-profit, non-taxable entities such as hospitals, universities, and churches own another 24 million square feet. Of course, we have seen many examples of such land being converted into mixed use.  But, it takes a bit more time to get there.
  • The District owns 34 million square feet, some by the District’s Housing Authority. These lands could be transformed into housing or mixed use, but the development must follow government procedures.  It takes time.
  • The United States government owns 197 million square feet of land (or 66 percent of the vacant and unimproved land). Of this, 133 million square feet are east of the river.
  • In zip code 20032 (Washington Heights and Bellevue neighborhoods) only, the District and the U.S. governments combined hold 53 million square feet of land.

What does this all mean? The Office of Planning estimates, under plausible conditions, the District could add 175,000 new households by 2040.  This would mean an increase in demand for over 200 million square feet of housing to house the new residents (see pages 27 and 28 here).

Twenty five years is not a very long time, so if we limit our new construction to privately owned vacant  land only, to meet the 200 million mark, we would have to see a floor-area-ratio of 4 or greater–that is the usable construction area is four times or grater than the size of the lot.  This is pretty dense.  But of course there are other sources of land including parking lots, older buildings or properties with underutilized capacity.

Here is a map of all vacant land in the District by zip codes by ownership and tax status. We made interactive maps, too, which you can get to using the link here or clicking on the map below.

image004

What exactly is this data? The data captures all lots and squares marked as vacant in the District’s real property database classified as commercial or residential.  We eliminated smaller lots unless they are in the same square and combined made 2500 sq. ft. of land or more. The zip code results might be influenced by where ownership records fall within large properties.