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




Under Five Population



Percent high school graduate or higher 86.9%














Percent Living in Poverty



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.

D.C.’s Immigrant Workforce

Around 829,000 people work in D.C. (within the city-proper), and about 26 percent of them are immigrants. Today, the Washington Post reports that some of D.C.’s immigrant workers, particularly those working in restaurants and some daycare centers and schools, are going on strike.

Indeed, the industries with people on strike have some of the highest concentrations of immigrants in D.C., as you can see in the graph below.  Seventy-one percent of chefs and head cooks working in D.C. (within the city-proper) are immigrants, as are 61 percent of lower-rank cooks. Fifty-seven percent of childcare workers in D.C. are immigrants. The occupation in D.C. with the largest concentration of immigrants is carpenters, 80 percent of whom are immigrants. (In our analysis we only looked at occupations with more than 3,000 workers in D.C.)

Most of the occupations with the highest concentrations of immigrants in D.C. are those with low or middle wages. However, immigrants comprise almost half of D.C. workers in several high-wage occupations: economists (46 percent), mathematicians and statisticians (43 percent), and physical scientists (42 percent).

We define a job as low-wage if its median wage was in the bottom 25 percent of median wages across all jobs in D.C (or below $44,000). High-wage jobs have median wages in the top 25 percent (or above $86,000) and middle-wage jobs are in between.

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While low-wage jobs in D.C. have the highest concentration of immigrants (41 percent of all low-wage workers in D.C. are immigrants, compared to 22% of middle- and high-wage workers), the number of immigrants in low-wage jobs in the city is roughly equal to the number of immigrants in high-wage jobs, since the city has many more high-wage workers. There are about 75,000 immigrants in low-wage jobs in D.C. and about 73,000 immigrants in high-wage jobs.


What exactly is this data?

Our data on immigrants by occupation comes from the 2015 American Community Survey 1-year PUMS data. “Immigrants” include naturalized U.S. citizens and non-citizens. “D.C. workers” are people who live in D.C., Maryland, and Virginia who report D.C. as their place of work. We only look at occupations with more than 3,000 people working in D.C. in order to reduce sampling errors. Because the ACS is based on a sample, there is a margin of error in all of our calculations. Our calculations should be treated as estimates, not precise counts.

Our wage data comes from the May 2015 Bureau of Labor Statistics Occupational Employment Statistics Survey. We define a job as low-wage if its median wage was in the bottom 25 percent of median wages across all jobs in D.C (or below $44,000). High-wage jobs have median wages in the top 25 percent (or above $86,000) and middle-wage jobs are in between. In cases where the occupation code in the ACS data did not match the occupation code in the BLS data, we calculated a median wage using the ACS data.


What is that extra bathroom worth?

The scenario is all too familiar- searching the web listings you come across the seemingly perfect 2- bedroom row house –it’s in the right location, close to public transit and good schools, not next to the fire station, it’s more than 12 feet wide so the stairs don’t eat up all the space, it has a decent size garden and a finished basement, it’s under $1 million.  Then it all comes crashing down -it only has one bathroom!

Even the prospective single buyer knows that while one bathroom might be perfectly suitable for her/his living arrangements, on resale this is likely to affect value and narrow the field of prospective buyers.

In this post we crunched the numbers to see how much an extra bathroom is worth and the difference with respect to a half-bathroom.

Here’s what we found based on an analysis of market values for over 15,000 2-bedroom single family homes.

Average value of a an additional full and a half bathroom in a 2 bedroom home in DC  


Difference in value compared to a 2 bedroom- 2 bath

2 Highlights:

On average, the extra full bathroom results in a difference in value of about 15 percent, or almost $60,000.

The average figure masks significant differences among neighborhoods.  In Georgetown, the differential between a one bathroom and 2 bathroom property was almost 25 percent.  Some of the differential is due to the fact that a 2 bedroom 2 bath house is simply larger- larger bedrooms, a larger living room- so that not all of the incremental value is due to the extra bathroom. We estimate that about 1/3 of the additional value or about $75,000 is due to the extra bathroom. (See graph below for a comparison of select neighborhoods)

In less expensive neighborhoods, like Trinidad and Brookland, the difference was less pronounced, only about 15 and 11 percent respectively.

Having 1.5 baths narrows the differential in value with respect to a full 2 bathroom considerably. The average difference in value compared to a full 2 bathroom is $23,000, or about 6 percent.

Comparison of values of 2 bedroom homes by number of bathrooms and neighborhood 


The upshot

The large premium that a 2 bedroom 2 bath row house commands over a house with only a single bathroom  makes it worthwhile to ask an architect friend if she/he can squeeze in at least a half bath. Perhaps they can combine the hallway closet with the kitchen pantry. This is certainly the case in neighborhoods like Georgetown where the increase in value from having the extra full or half bathroom would compensate for the cost.

What exactly is the data?

Data on property values were obtained from the Office of Tax and Revenue and refer to values in 2015. As noted above these are average differences.  Values will vary considerably based on others factors such as the condition of the home, square footage, the presence of other amenities like fireplaces, garages, etc.

Bob Zuraski contributed to this post


D.C.’s Cashiers and Janitors Are More Likely to Live in the City than Other Workers, but That’s Changing

Nearly 800,000 people work in the District of Columbia, yet only about 30 percent of the District’s workers live in the city-proper. Workers in low-wage jobs are more likely to live in the city than those in middle- and high-wage jobs. Thirty-nine percent of D.C.’s workers in low-wage jobs lived in the city between 2010 and 2014, compared to 30 percent in middle-wage jobs and 27 percent in high-wage jobs.

We define a job as low-wage if its median wage was in the bottom 25 percent of median wages across all jobs in D.C (or below $44,000). High-wage jobs have median wages in the top 25 percent (or above $86,000) and middle-wage jobs are in between.

You can see how this plays out by occupation in the graph below. Cashiers, janitors, childcare workers and others in low-wage jobs are more likely to live in the city than most other workers, though people in a handful of middle- and high-wage occupations, like managers of social and community services, teachers, and chief executives, have relatively high rates of living in the city too. Registered nurses and police officers (which include transit and federal police) are the least likely to live in the city.

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Here’s where these workers live:

(click to interact)map3

People in low-wage jobs tend to live in the city more than others, but that’s been changing over the past decade, as you can see in the chart below. The city is losing construction workers, cashiers, childcare workers, and janitors, and gaining people in high-wage jobs, like managers of social and community services, operations research and management analysts, and economists.

In less than a decade, the workers most likely to live in the city shifted from cashiers, retail salespersons and clerks (50 percent lived in the city in 2005-2009) to managers of social and community services (47 percent lived in 2010-2014).

Meanwhile, over the same time period, the least likely to live in the city switched from software developers (9 percent in 2005-2009) to police officers (11 percent in 2010-2104).


As the graph below shows, this is part of a larger pattern of D.C. workers in middle- and high-wage jobs starting to show a preference for living in the city, and workers in low-wage jobs increasingly living in the suburbs – a trend that’s unsurprising given the District’s increasing cost of housing. The percent of workers in low-wage jobs living in the city decreased from 43 percent to 39 percent between 2005-2009 and 2010-2014, while the percent of workers in high-wage jobs living in the city increased from 24 percent to 27 percent over the same time period. These changes may seem small, but they are statistically significant at the 99 percent confidence level.

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What exactly is this data?

Wage data: Our wage data comes from the Bureau of Labor Statistics Occupational Employment Statistics survey of D.C. workers from May 2015. We define a job as low-wage if its annual median wage was in the bottom 25 percent of annual median wages across all jobs in D.C (or below $44,000). High-wage jobs have annual median wages in the top 25 percent (or above $86,000) and middle-wage jobs are in between.

Percent of Workers Living in the City: Our data comes from the American Community Survey PUMS data for 2005-2009 and 2010-2104. For our universe of D.C. workers we started with everyone living in D.C., Maryland, or Virginia who works in D.C., so we are excluding long-distance commuters who work in D.C. but live in places outside of D.C., Maryland, and Virginia. When we analyzed specific occupations, we looked at all occupations with 8,000 or more workers in D.C., with the exception of miscellaneous managers since the category is vague. We grouped some occupations together so they surpassed our 8,000 person threshold.

Map of Where Workers Live: This data comes from the American Community Survey PUMS data for 2014. We only look at workers who work in D.C. and live in either D.C., Maryland, and Virginia. All of the occupation groups in the map have 8,000 or more people working in the city.

Police Officers: Police officers in this case includes more than just people employed by the Metropolitan Police Department; it also includes transit police, federal police, and police who said they work for private organizations. In 2014, the Metropolitan Police Department released data showing 17 percent of its officers live in the District.

Errors: The data in this post have various margins of error since the data comes from surveys. In most cases we used a five-year data set to reduce the errors, and only looked at occupations with 8,000 people or more. The errors are highest for the map of where people live because for that we had to use a one-year dataset (geographic boundaries changed within the five-year dataset, making a map more difficult to produce). The map is intended to give readers a general sense of where people live; we discourage people from using it for direct area-to-area comparisons. Our findings on the loss and gain of workers of different occupations and wage levels are in many cases statistically significant and we have noted this in the post.


District injury death rates down since 1999

The incidence of death by injury—which includes accidental and intentional deaths—has been on the decline in the District. The rate of death by injury decreased by 12.7 percent between 1999 and 2014, despite a steady national increase in the rate over that time. The injuries counted include accidental deaths such as a falls or drug overdoses, as well as intentional deaths such as murder or suicide. The actual number of deaths by injury in 2014 (385) is about the same as in 1999 (382), but because the population in the District has steadily increased over that time, the rate has decreased. Only two other states – Alabama and Nevada – saw a decrease from 1999 to 2014, and the District had the largest percentage rate decrease of any state. While some year to year fluctuations occurred in all the states throughout the 15 years, most states have seen a steady rise in death by injury rates.image001.png

Until 2010, the District stood apart from Virginia and Maryland as the incidence of death by injury in the city was above the national level. Now the District is more like its neighboring states, all three of which are now below the national rate. image003.png

The decline in intentional injury deaths (murder and suicide) is the major factor in the overall decrease in the District’s injury death rates.  Compared to 1999, intentional injury death rates have decreased by 40% in the District, while accidental deaths rates have increased by about 17%, although both categories have been somewhat erratic over the period.


Accounting for Population Size

It may be argued that comparison to the national rate is not useful, given the District’s small population. So we also looked at how the District compared to the four least-populous states, two with fewer people than the District (Vermont and Wyoming) and two with more people (Alaska and North Dakota).  We found the incidence of death by injury has increased in all four of these states.  Alaska’s injury rate grew 11%, Wyoming grew 15%, North Dakota grew 25%, and Vermont grew a whopping 53% increase over 1999 rates.


While intentional injury death rates decreased significantly in the District, the other least populous states saw increases.image009.png

All of the least populous states saw increases in accidental injury death rates.image011.png

Could the growth the District has experienced over the last 15 years be playing a factor in the injury death rate decrease?  If so this would be unique to the District.  States with population growth rates higher than the District experienced an injury death rate growth of an average 13.7%, while states with population growth lower than the District experienced an average injury death growth rate of 28.8%.  But individual state rates vary so widely, this is not sufficient explanation. Population growth may play a part in our lower injury death rate, but nearly all states saw an increase, so the District’s 12% decrease still stands out.

Other Demographics

When we break out the total deaths by sex and race, some of the totals are too small to analyze on a year to year basis.  However, we can report that overall injury death rates for whites in the District have increased 43% since 1999, while rates for African Americans has decreased by 7%.  These results are driven mainly by the reduction in intentional injury death rates for African Americans, and an increase in accidental injury death rates for whites.

Injury death rates for females in the District have increased 62% since 1999, while rates for males have decreased 28%.  Boys and men are still more likely than girls and women to die of injury, which fits with earlier research (, but the trend away from this stands out.


What is this data?

The data source is the Fatal Injury Data in the National Vital Statistics System (NVSS) operated by the National Center for Health Statistics under the Centers for Disease Control. (

DC’s startup economy- How much does it pay to work at a startup in DC compared to other companies and other cities?

Start-up companies play a vital role in the economy, fostering innovation and providing job opportunities for those who want to go at it on their own and/or prefer to work in what is typically a less hierarchical environment.  In the digital information era, the glorified image of young entrepreneurs and workers who start hugely successful companies masks some of the risks associated with working at startup companies. Typically these companies do not have the deep pockets to pay salaries comparable to established companies and failure rates among startups tend to be higher than for established companies.  This may be a risk worth undertaking as the payoffs for working at start-up companies that eventually become successful can be significant, particularly in high tech companies that go public. For younger individuals, job security and pay related to seniority and tenure can be less of a factor than for older individuals, making the risk of working at a startup less severe.

In this post we examine how average salaries for startup companies compare to salaries across companies in DC and other cities. We use industry-wide data for all age groups and then show this separately for 25-34 year olds.

Table 1: Average Salaries for Startups vs All Companies, All Age Groups


Source: US Census Bureau,

  • As shown above, for all age groups, salaries at startups ranged between 56 percent and 76 percent of salaries at all companies among the comparison cities.
  • DC was at the lower end of the range at 61.2 percent exceeding only NYC at 56.2 percent.

Table 2: Average Salaries for Startups vs All Companies, 25-34 year olds



Source: US Census Bureau,

  • For the 25-34 year old age group the ratio of start-up salaries compared to all salaries was higher than the average for all age groups shown in Table 1. This is likely related to the fact that salaries for younger individuals are typically lower and more compressed to begin with.
  • The ratio of pay varied considerably among cities.
  • In San Francisco and Austin, the pay for young individuals working for startups was comparable to the pay at more established companies.
  • In San Francisco the pay for 25-34 year at startups exceeded pay for all other age groups.
  • DC and New York were at the lower end of the scale again. Pay at startups was, respectively, 72.7 percent and 68.5 percent of the pay at more established companies.

Here is a summary graph of the pay ratios for all ages and the 25-34 year old age group.

Graph 1: Ratio of Salaries at Startups Compared to all Companies, Ages 25-34 and All Ages   3

Source: US Census Bureau,

Finally we looked to see if there was considerable variation among select industries that could explain some of the differences in pay at startups in DC compared to San Francisco for instance.

Here’s what we found:

Table 3: Startup pay to ratio to all companies among industries in San Francisco and DC, Ages 25-34


Source: US Census Bureau,

Notably, in San Francisco the pay ratio for Professional Scientific and Technical Services, one of the largest industries for tech startups, far exceeded that in DC, almost 100 percent compared to 78 percent.

With the exception of Health Care and Social Services, pay ratios in the other industries also exceeded or were similar to DC.


The difference in pay ratios for start-up pay likely reflects a more vibrant start up economy in San Francisco and Austin, compared to more traditional career paths in established financial and legal services firms in DC and NYC. The causes for this could include- stronger ties to venture capital funding that provide greater financing to startups , or simply stronger competition for young talent among startups in San Francisco and Austin.

What exactly is the data?     Data on wages is from the U.S. Census Bureau, Local Employment Dynamics Data for 2014. Start-up companies are defined as firms that are less than 4 years old.

Bob Zuraski contributed to this report         

Resident employment grew four times faster in DC than in the suburbs over the past 4 years

According to the US Bureau of Labor Statistics, the number of employed DC residents rose from 323,823 in April 2012 to 370,204 in April 2016, a 14.3% increase of 46,381. This increase stands out in the context of recent labor market trends in the US and in the Washington metropolitan area:

—The percentage increase was more than twice that in both the US economy (6.6%) and four times the increase in the DC suburbs (3.5%),

—The increase represented almost one-third of the increase in the entire metropolitan area, although DC’s regional resident employment share is just under 12%.

—The percentage change in DC’s resident employment was more than twice the increase in jobs located in DC. (14.3% v. 6.2%). For the US and the rest of the metropolitan area, resident employment actually grew a little less rapidly than wage and salary employment.

—The increase, averaging 11,595 per year, is about equal to the growth in DC’s population over that period.

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Three places to look in helping to explain this remarkable increase in employed DC residents are: (1) growth of wage and salary jobs in DC, (2) unemployed persons returning to work, and (3) labor force growth. As noted below, all of these have contributed, but the most important explanation lies with labor force growth and related dynamics, particularly population growth.

Wage and salary employment located in DC. DC employers added 45,467 wage and salary jobs from April 2012 to April 2016, about the same number of jobs as the increase in resident employment. These new jobs could certainly be a source of employment for additional DC residents. Although DC’s jobs grew a little faster than those in the suburbs, there was, however, nothing very unusual about this increase. DC’s share of the new jobs in the metropolitan area over the past four years (26.6%) was close to its recent average share of all metropolitan area jobs (about 24%).


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A growing job base no doubt helps to attract workers to the District of Columbia, but job growth in DC cannot explain why employed residents grew by 14.3% while jobs grew 6.2%. It should be noted that from April 2012 to April 2016 the percentage increase in resident employment (6.6%) in the US economy was actually a little less than the 7.5% wage and salary job growth, and the Washington metropolitan area growth pattern was similar, albeit a little slower—4.6% for resident employment and 5.6% jobs.


Unemployment. DC unemployment declined by 8,481 from April 2012 to April 2016, which represents about 18% of the increase in resident employment. However, falling unemployment cannot explain why resident employment increased so much faster in DC than elsewhere. DC’s percentage decline in unemployment was less than in the Washington metropolitan area suburbs and the US.

Looked at another way, over the past four years, it took an increase of 5.5 DC employed residents to reduce unemployment by one (46,381 increase in resident employment divided by 8,481 decline in unemployment). In the US the ratio was 2.1 new employed resident for every decline of one in unemployment, and in the suburbs the ratio was 1.9 new employed resident for every reduction of one in unemployment.

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Labor force dynamics. By definition, the increase in resident employment must be equal to the sum of the reduction in unemployment plus the increase in the labor force. Consequently, over 80% of the growth in resident employment is accounted for by labor force growth.


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Along with resident employment, the increase in DC’s labor market represents another unusual change over the past four years. The 10.6% increase in DC’s labor force was 3.7 times greater than in the US (2.9%) and more than 7 times greater than in the suburbs (1.5%). With about 12 percent of the region’s labor force, DC accounted for 46.5% of the region’s increase over the past four years.

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Population growth is the principal reason why DC’s labor force is rising so significantly. Over the past four years DC’s population grew 7.5%, compared to 4.8% in the suburbs and 3.2% in the US. If DC’s labor force had grown at the same rate as population, the labor force would have grown by 26,617. This growth in labor force due strictly to population would account for about 70% of the 37,901 labor force increase, and 57% of the 4 year increase in DC resident employment. About 30% of the labor force increase, however, is related to factors other than population growth. These factors cannot be explained by this data. For example, DC’s rising population may have an unusually large share of people in the labor force. Or the entire population may be changing so that the proportion persons in the labor force is rising. Or rising employment opportunities may be pulling more of the people who have left the labor force into DC’s labor market, although it is not obvious why this should be more true in DC than elsewhere.

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Where do the additional DC employed residents work? The BLS data used in this survey do not indicate the place of employment for DC residents. The increase in DC resident employment from April 2012 to April 2016 is the result of some combination of (1) a portion of the increase in new wage and salary jobs added in DC, (2) DC residents filling jobs formerly held by commuters who retired or otherwise left their positions, (3) additional DC residents commuting to the suburbs, and (4) additional DC workers who report they are working but are not as wage and salary employees.

The importance of commuting patterns is underscored by trends in suburban jobs and resident employment over the April 2012 to April 2016 period. During those four years suburban resident employment growth was far below the percentage change in jobs located in the suburbs (3.5% v 5.4%), and the increase in wage and salary jobs exceeded the growth of resident employment by more than 30,000. This difference between job growth and resident employment growth in the suburbs would appear to provide employment opportunities for DC residents commuting to the suburbs—and employment opportunities as well for persons commuting from outside of the Washington DC metropolitan area. In addition, the relatively slow growth in the suburban labor force could indicate a slowing of interest in commuting to the District of Columbia.

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What is this data?

This analysis of labor market trends in the US and the DC area covers the period from April 2012 to April 2016, a time that includes the most recent four years of recovery from the Great Recession. (Recovery from the recession officially began in June 2009.) The analysis uses data from two Bureau of Labor Statistics surveys that are conducted each month: (1) wage and salary employment data by place of work and (2) labor market data by place of residence, which includes labor force, resident employment, and unemployment. The data for April 2012 and April 2016 are three month averages for February, March, and April. Population data for the first quarters of 2012 and 2016 are from Moody’s Analytics.

It should be noted that the data presented here can be revised as Census and BLS sort through additional information that becomes available to them.