High-Income Residents: Are They the Driving Force Behind DC’s Premium Apartments?

In a recent post, we concluded that the premium apartment rental market is the more popular and ascendant segment of the city’s housing market in the context of the current trend in net population growth. To further elaborate on this topic, we profile the tenants in the city’s Class A and Class B apartment buildings built after 2000 based on income tax data characteristics. The full research paper can be found here.

Economic Profile of Tenants

Table 1 tells us that in 2015 half of the residents who were income tax filers in the 88 Class A and Class B large and mid-sized apartment buildings that were built after 2000 had annual reported income of less than $57,428 and were under the age of 31.5. And, the vast majority of these tenants were single tax filers (unmarried and no dependents) and were relatively new[1] to the city.


[1] We classify a new resident as someone who existed in the city’s income tax data in either 2013, 2014, and/or 2015, but did not exist in 2012 or prior.

Who is more likely to live in new apartment units?

Our data shows that there was a tripling in the number of premium apartment units delivered in 2013 compared to 2012. To better evaluate the data, we divided the buildings into two groups. The first cohort is comprised of all 2015 tax filers found to be residents in multifamily buildings that delivered between January 2000 and December 2012 (relatively older premium multifamily buildings). The second cohort is comprised of all 2015 tax filers found to be residents in multifamily buildings that delivered between January 2013 and December 2015 (newer premium multifamily buildings).  We then fit a statistical model to the data to determine the characteristics of new buildings versus older buildings.

Using T-tests, we find that the newer buildings tended to have units that were an average of 88.3 square feet (10.5 percent) smaller and cost 17.5 percent more per square foot (Table 2). We also found that individual tenants in newer buildings tended to have income that was on average of $9,884 (12.3 percent) less and 1.3 years younger than renters in older buildings.


Using a statistical model to differentiate the characteristics of tax filers living in a newer building in 2015 versus older buildings, we calculate the probability that certain factors affect the choice of residing in newer apartment buildings instead of older buildings.

While the tenants in new and older apartment buildings are generally very similar, we were able to again tease apart a few distinctions in the two populations as well as a few contributing factors for their housing choices.


We find that income has almost no influence on whether a resident chooses to live in a newer or older apartment building (for every $100,000 increase in income, the probability to choose a newer building increases only about 4 percent). Age is also an important factor in determining how likely a resident will choose newer or older apartment units. Younger residents are more likely to reside in newer apartment buildings. For each additional year in age, existing residents are 0.8 percent less likely to reside in newer buildings, while this percentage is 0.2 percent for new residents. We also find that tenants commonly supplement their traditional wage/salary income with additional business income from entrepreneurial or other self-employment endeavors.[2]

Given that 83 percent of all tenants in these buildings are single filers (as shown in Table 1), we find that long time city residents who are head of household tax filers (unmarried income earning adults with dependent children) are 23 percent more likely to live in newer buildings compared to married residents. This is possibly due to the city’s affordable housing efforts to place low-income households in these new buildings via affordable housing programs.  And finally, single residents are more likely to reside in newer buildings compared to married filers, especially when they are relatively long-time residents.

[2] On government tax forms, adjusted gross income is comprised of wages and salaries, business income, investment gains or losses and other income.

Several Ways DC is Changing

In sum, we find the following results. First, 64 percent of the tenants in all the apartment buildings in this study tended to be new to the city. Second, the newest apartment units are smaller and more expensive, and their residents tended to be slightly younger and have less income than residents in the relatively older buildings. Third, residents in the newest units are more likely to have business income as part of their total reported income, which suggests there is an increased tendency for these residents to supplement their traditional wage and salary income with additional income from entrepreneurial or other self-employment endeavors. Lastly and surprisingly, the analysis shows a relatively strong increase in probability for residents in the newer buildings to be head of household filers. This is possibly due to the city’s affordable housing efforts to place low-income households in these new buildings via inclusionary zoning and various housing subsidy programs.

Conventional wisdom assumes that these newer buildings are attracting primarily high-income residents; however, we find that compared to older buildings, the city’s newest and pricier apartment buildings built during the recent residential construction surge (2013 and after) tend to attract a higher percentage of new residents to the city, and also attract a higher percentage of single, young residents with income below the city average. It appears that both the city’s demographics and apartment rental market are continuing to evolve and change in significant ways. And, it is very likely these changes will have considerable implications on the residential and economic patterns of the city in the years to come.


The Data

Using data from CoStar, we identified 88 Class A and Class B large and mid-sized apartment buildings (containing 21,203 total residential units) from across the city that were built after 2000. The list can be found here. This study also uses 2015 individual income tax data for all DC tax filers who listed their home address as being in one of the 88 apartment buildings mentioned above.



Gender pay gap among the District’s workforce

The gender pay gap refers to the systematically lower wages women receive relative to men with similar talents and responsibilities.  A White House study tells us that the median wage for women equaled only 77 percent of the median wage for men across the nation–a pay gap of 23 percent. The pay gap exists between men and women with similar degrees, similar work experiences, and similar occupations.  The study mentions various factors that create this gap: family responsibilities such as child rearing generally fall on the women; or men tend to negotiate and seek promotions more aggressively.

But explanations offered for the gender pay gap, more often than not, do not settle the matter; rather they lead to new debates. Economists from the Federal Reserve Bank of St. Louis, for example, point out that broad comparisons are not appropriate for gender gap analysis and one must take into consideration differences in attainment, experience, and occupational choice as well as the intensity with which someone works. They argue that when one accounts for these differences the pay gap between men and women declines to five percent. There are even disagreements of the effects of childbirth: some argue that high-skilled women experience a smaller wage effect from taking time off for childbirth because of the demand on their skills, whereas others argue that the declining the declining birth rate among high-skilled, highly-paid women is evidence that childbirth hurts long-term wages.

We became curious: How about the District’s workforce? In this city with many workers on government pay schedules, do we have a gender pay gap that is significant? How does the gap change with age, marriage, education level, hours worked, or occupation? We looked at the annual wage and salary earnings of men and women.  (In this study, we use average wages, and not the median. The median salary is useful when data has very extreme values. But we are relying on American Community Survey for data, which already caps incomes and therefore throws out extreme values. Even then, the pay gap to some extent reflects the dominance of men among very highly paid workers. For example, among those who receive wage or salary income of $500,000 or more, men outnumber women by more than 3 to 1.) Here is what we found:

Overall gender pay gap

2014 data from the American Community Survey tells us, in that year, women made up 48 percent of the workforce (the nationwide share is 47 percent) and received only 42 percent of the wages. The average salary for women over the age of 21 in 2014 was $71,000 and for men it was $90,000. That is, women’s annual wage and salary earnings were 78 percent of men’s; putting the District’s gender pay gap at 22 percent compared to the national gap of 23 percent.

Gap by type of employer

The pay gap is greatest among workers of the private entities. Among for-profit firms, women, on average, receive 76 percent of the average wages paid to men; among non-profit firms, the comparable number is 77 percent. It appears that the government sector has a smaller pay gap, with a 16 percent gap among state and local government employees (largely D.C. government but also employees at charter schools, UDC, and other local entities); and the gap is 15 percent among federal government workers.

Has the gap been closing over time? To see this we look at the wage differentials among older and younger cohorts of workers. The gap generally widens by age across all four types of employers. For example, among for profit firms, the pay gap is only 7 percent for workers between the ages of 25 and 34; among the workers of non-profit firms, we don’t see a gap at all. However even when the gap is small at this age group, it does not tell us definitively that the gap will continue to be smaller when this cohort ages. This is because a small gap can widen over time even if the two groups received the same percentage increase in their wages. Viewed from this angle, it appears like the government sector, especially the federal government, has promotion practices that are most gender-blind, as the gap does not widen as quickly across cohorts.


How about education? The data suggest that in the private sector, having a college degree (or higher) does not necessarily close the pay gap.  This is significant, but is still not a definitive indicator of gender-dependent pay or discrimination. For example, if more men study STEM and gets jobs in science and technology fields, and more women study social sciences or humanities that feed into lower paying jobs, then a pay gap will exist for given degree in education. Data do tell us that 70 percent of STEM degrees awarded at universities and colleges indeed go to men.  This we can also see at the doctoral level: even though more women receive doctoral degrees than men in a given year, women are extremely underrepresented in STEM fields; they are also less likely to get a degree in physical sciences and business programs, which tend to lead to higher paying jobs.  But, even with professional degrees (doctors and lawyers) there is a significant gap of 25 percent between men and women.

Finally, for DC residents who make up a third of the District’s workforce, the gender gap is smallest at the federal government and private, for-profit firms, and widest among DC residents employed in a state or local government job.

Is there a marriage toll for women?

Among men and women who have never been married, the gender pay gap is only 6 percent. Among men and women who are married, the gender gap is 23 percent. A single woman (whether married or divorced) between the ages of 45 and 54 makes more than a single man in the same age group. A married woman of the same age group makes 23 percent less than married man in the same age group. Why could this be? Perhaps married women in this group work fewer hours, or they took time off to raise children, or took more flexible but lower paying jobs. In fact, the gap is much smaller among married women under the age of 34 (and presumably without children).  We can think of many other possible contributors: married men are happier, and perhaps for this reason more productive. (They make more than unmarried men as well: see here and here). Because their wives take over responsibilities at home, perhaps married men can dedicate more time to their work. Perhaps there are evolutionary reasons: men who have not been picked for a marriage by the age of 45 are just low in productivity. Perhaps it is selection bias in the other direction: women who are aggressive about their careers do not get (or stay) married. Perhaps employers low-ball the salaries of married women expecting that they would take more time off at some point in their career and perhaps just leave. Again, we do not have definitive answers.


Work hours

Gender pay gap is a feature of full time work.

When we look at men and women who work fewer than 30 hours, we see more women than men, and their wage and salary earnings are generally greater than men’s. But the size of District’s part time workforce is small—it stands at less than 10 percent of the total workforce in the District. Among those who report working 30 to 40 hours a week, the gender pay gap is 11 percent (464,000 workers report working 30 to 40 hours and half of them are women.)  Among those who report working over 40 hours (approximately 250,000 workers, 41 percent of whom are women) the gender pay gap is 21 percent.

This is a perfect example of how the same information can support exactly opposing policy solutions. One can look at the gap and see the need for interventions; others can look at it and conclude that the labor market can create many types of jobs that require different combinations of effort and wage that fit the needs, abilities or desires of all kinds of different workers. If that is the case, interventionist policies would actually reduce employment and hurt women more.


Industry and occupations

Dominance of women in a given industry does not guarantee a better pay.  For example, 70 percent of workers in the health care industry are women.  This includes not just people with medical occupations such as doctors and nurses, but also all other types of workers from managers to computer programmers who work for a healthcare company. In this industry, the gender pay gap is 36 percent. Women in construction—a male dominated industry—make more than men but this is because they are more likely to hold managerial or creative jobs, rather than working on the construction site.


So perhaps we should compare men and women doing the same job. Cutting and slicing the data gets precarious when we go down to individual occupation level, but we looked at occupations with more than 2500 men and 2500 women, and found only a handful of occupations where the pay gap is small or favor women. Physicians and surgeons in the District face a gender pay gap that is as wide as the healthcare industry: 37 percent! Looking at the lawyers—the gap is 73 percent. We can string together more explanations: more women go in-house (lower pay) or most women lawyers are younger.  But some other outcomes are puzzling: 63 percent of education sector employees are women; the industry gender pay gap is 23 percent. Among elementary and middle school teachers only (we are excluding administrators, and we don’t know if they are public charter school,  DCPS or private school teachers), the gap is 20 percent. Why?  What explains the gap for cashiers among whom women receive only 63 percent of what men earn? How about waiters and waitresses where the gap is 11 percent? Do they get fewer hours of work or does gender, ethnicity, or other characteristics–of the wait staff or the customers–play a role in how we tip waiters and waitresses?


Survey data could be idiosyncratic, especially given the small size of the District’s sample (but in this case we are using data from three states). Still the pay gap is there, even when one controls for age, marital status, or even down to occupation. Is this a sign of flexibility in the labor market? If all jobs required 40 hours a week (no more, no less), had the same degree of flexibility (or inflexibility) would the labor market outcomes be better for women? Or do firms knowingly take advantage of lower reservation wages for women? If so, why do women accept lower salaries when everything tells them to negotiate? There has been research that says women can be penalized for negotiating if they negotiate like men, suggesting that gender roles continue to play a role in the workplace.

We wonder what our readers think.

What exactly is this data? We use the American Community Survey micro data for 2014. We combine District of Columbia, Maryland, and Virginia data for respondents who report working in the District. Wage and salary income is what respondents report earning in a year.


Kids in the neighborhood: The District has more children, but they are not where they used to be

After many years of decline, the total number of children in the District started to increase beginning 2011.


The turnaround in the overall population had happened earlier: the resident population bottomed out in 1998, remained flat for another eight years, and then it grew, and fast, especially since 2008, when we began adding over 10,000 net new residents each year. As we have shown here, most of the newcomers had been singles without children, who started replacing the families who had left the city. Only since 2011, the number of children began to grow, and in the last four years, the child population has grown at a faster rate than the adult population.


Separating children under the age of 6 and school-age children (ages 6-17) reveals differences in population trends.  The numbers for the younger children started recovering much earlier: the first positive numbers we see date back to 2005 and the growth has been steady since 2008 for this group. Among the older kids, however, the population slump began later than the younger kids (roughly 2001, compared to 1995 for children under 6), but it also recovered much later. So when we look at longer-term trends, we see that today we still have 3,500 fewer school-age children than we had in 1990, whereas we have 6,000 more children under the age of 6.


The familiar story we often hear—young people who moved into the city in early 2000s leave once their children hit school age—might be coming to an end.  Since 2011, we do not see—on net—the exodus of children from the city. But they now live in different neighborhoods.

To see the shift in where families live, we use the five-year summaries from the American Community Survey, which include data at the census tract level.  We use data from the 2010-14 summary, which combines five years of data and provides an average.  We then compare these to the 2006-10 data. Because the data are averages for five years, they don’t capture year-to-year variations.  For example, the five-year data summary does not show the recent growth in the number of school-age children, as it includes two years of decline (2010 and 2011), which overpower the three years of growth (2012, 2013 and 2014).  (But five-year-summaries are our only choice for this analysis. Because census tracts are small, the annual data are very unreliable; ACS does not even publish them.  Adding five years of data together makes things a bit more certain, but the error terms could still be large.)

We begin with where the children live. The map below shows the number of children in each neighborhood for the five-year period that ended in 2014. Most children are found in residential neighborhoods outside of downtown, especially in the neighborhoods just east of Rock Creek Park and at the southeastern edge of the city. The south east neighborhoods are among those that have the highest share of children in their populations: 30 percent in the Congress Heights, Bellevue, Washington Highlands cluster according to the 2010 census, and 34 percent in the Douglas and Shipley Terrace neighborhoods (though these numbers are well below their historical levels).  In contrast the share of children is under 20 percent in every neighborhood west of the park (the average for the city is 17 percent), except for Chevy Chase, where it is 23 percent (and increasing), 15 percent in Columbia Heights (where it is decreasing) and 20 percent in the Petworth cluster (where it has held somewhat steady). image016.png

Here is the interactive map.

How has this map changed since 2006? Below we show the change in the number of kids in each neighborhood in the last five years compared to the 2006-2010 period. You can see that that neighborhoods around the 16th Street and Georgia Avenue corridors north of Columbia Heights have attracted many families with children. The Takoma, Manor Park, and Brightwood Park cluster have added over 1,000 children, second only to the extremely popular Brightwood Park, Crestwood, Petworth neighborhood cluster, which added over 2,000 children. Capitol Hill now has fewer children (but only a modest decline of 100).   There are 1,280 fewer children east of the river now, but when broken down by neighborhood clusters, we see that five out of the 11 neighborhood clusters added more children (the biggest gains were in the Garfield Heights, Fort Stanton and Knox Hill cluster, which has seen lower vacancy rates and increasing home ownership).  Eastland Gardens and Kenilworth, on the other hand, lost half the children who lived there between 2010 and 2014 (a continuing trend for these neighborhoods), and the River Terrace, Benning, Greenway, Dupont Park cluster, which has been losing housing units and seeing increasing vacancy rates, also lost one third of the kids in the neighborhood.


Here is the interactive version of this map.

Families with young children make very different choices about where they live compared to families with school-age children.  They are more likely to move to the city, or stay in the city after having a baby, and could settle in any one of the neighborhoods. The map on the left below shows that one finds more children under the age of 6 in almost every neighborhood, but especially in the area that borders Rock Creek Park: neighborhoods along the 16th Street and Georgia Avenue corridors,  from Colombia Heights to Takoma Park, added  2,758 children under the age of 6. This is over a third of the net increase for this cohort. The notable exceptions to the growth are the neighborhoods that lie on the north west of the park  and a few neighborhoods east of the river, especially the River Terrace, Benning, Greenway and Dupont Park cluster. It is possible that young families are being priced out west of the park. Or perhaps, young parents are staying in the neighborhoods they lived in before they had kids.


Here is the interactive map.

Families with school-age children are pickier about where they settle. Neighborhoods north and west of downtown are adding more school-age children, neighborhoods that lie to the south and east of downtown continue bleeding.  There are more school-age children west of the river, but this is possibly aging-in-place as children who are included in the 0-5 group in the 2006-2010 data summary migrated to the school-age group in the 2010-2014 summary. Neighborhoods west of the park added 1,688 school-aged children. In comparison, the Brighwood Park, Crestwood, and Pethworth cluster, just by itself, added another 1,298. Columbia Heights, another strong destination for families with young children, cannot appear to hold on to the families with older children.  The cluster that holds Columbia Heights and Mt. Pleasant lost 574 children between the ages of 6 and 17. This is also true for the Brookland, Brantwood, and Langton cluster.

The biggest declines are in the neighborhoods east of the river, which collectively lost 2,654 school-age children. Losses were greatest in the neighborhoods along the northern border with Maryland.  The neighborhoods in these clusters, similar to the south east neighborhoods, used to have very high concentration of children but have been losing them since 1980s. For example 35 percent of the Eastland Gardens and Kenilworth’s population are children, down from 39 percent in 2000. In the Deanwood, Burrville, Grant Park, Lincoln Heights, and Fairmont Heights cluster, the number of children in 2010 was half of what it was in 1980. This neighborhood is now adding younger children, but not yet, school-age children. And given housing prices in the city and household incomes east of the river, most families who left these neighborhoods have likely moved out, and did not relocate in another neighborhood in DC.

The population dynamics across neighborhoods provide yet another picture of gentrification and where we are most likely to find it.  The city’s most expensive neighborhoods (when it comes to housing) are holding on to the school-age children but are not able to add young families. While east of the river continues to have the highest concentration of children, if trends continue, neighborhoods near the 16th Street and Georgia Avenue corridors could claim this distinction soon. Neighborhoods east of the river are adding younger children, but rapidly losing school-age children, and on the net losing families.

What exactly is this data?

There are 39 neighborhood clusters in the District, and unlike wards or census tracts, they are not drawn to be of similar size. Some are very small, others are large and densely populated. For example, the Brightwood Park, Crestwood, Petworth neighborhood cluster has the greatest number of children (8452), but it also has many adults, so the share of children in its population is only 20 percent.

We compile the neighborhood data from the five-year ACS data summaries, which include census-tract level data, using the tract-to-neighborhood cluster mapping from neighborhoodinfodc.org.

Data on child population is from kidscount.org.


District’s retail sector employs many more workers, but pays less

District’s retail industry is relatively small: it accounts for just under 3 percent of all employment in the District compared to about 9 percent in the Metro area and 11 percent in the nation. But this sector is changing rapidly in what it sells, how it sells it, and whom it hires to sell.

Retail establishments in the District are employing more people than they ever did before. In March of this year, District retailers employed 22,500 workers—a third more than they did in 1998. Most of this growth, however, is recent. Between 1998 and 2009, the retail sector employment growth was tepid and lagged behind overall employment growth in the city. This changed after 2010 following the rapid population growth. As large retail stores, supermarkets, and wholesale shopping clubs like Costco began moving into the city, the retail sector added 4,000 new employees. This represents a growth of over 23 percent between 2010 and 2013, four times faster than the rate at which overall District employment grew during this period.


Retail sector today is also more consolidated. Despite the growth in total employment, we have fewer retail establishments: 1,700 in 2013 compared to over 2,000 in 1998 and an average retail establishment now employs nearly 12 workers compared to about eight in 1998. The mom-and-pop stores with fewer than five employees are still the majority of retail establishments in the District (52 percent both in 1998 and 2013), but they are now down by about 15 percent in numbers since 1998. One quarter of establishments with five to nine employers are gone: the number of such stores declined from 488 in 1998 to 355 in 2013. During the same period, the number of establishments that hired 20 or more employees increased from 183 to 211 (but with fluctuations suggesting large turnover even among these establishments) and these mid- to large-sized stores are now 20 percent of all retail establishments, compared to 17 percent in 1998.


How about sales? Annual data on sales are not publicly available, but the quinquennial Economic Census allows us to look at the 1997 to 2012 period. This data show that retail establishments sold $4.4 billion worth of goods in 2012, up 13 percent since 1997 after adjusting for inflation. Almost all of this growth happened since 2002, especially between 2002 and 2007. District’s retail landscape also changed: sales shifted from general merchandise and big-ticket items such as building materials to food and drinks, personal items and clothing, and furniture and home decorating items. For example, in 1998, the District had about 30 general merchandise stores (including four department stores) which collectively accounted for 6 percent or retails sales and hired 1,400 (part time or full time) employees. In 2012, we had so few of such stores that the Census did not disclose their data. We lost 20 establishments that sold old or new cars or vehicle parts. These stores used to account for five percent of all sales volume, but now are of little significance. We lost 30 stores that sell books or other print materials, half the hobby stores (left with ten), and over a third of our gas stations. On the up side, we had 22 more grocery stores in 2012 compared to 1998 (and have added more since) and pet stores are up from 3 to 10. Pharmacies, drug stores, perfumeries, and beauty stores now account for one fifth of the retail sales compared to one-tenth in 1998. We have more places we could shop for athletic shoes or yoga clothes and the sales volume of these stores increased by over 30 percent.


Despite the growth in the retail sector sales and employment, total payroll at retail establishments remained stagnant and earnings per employee, after adjusting for inflation, do not appear to have increased. In 1997, a retail worker in the District took home what would have been the equivalent of $25,642 today. In 2012, earnings were up by only about $1,000 compared to 1998, but down from earnings from 2007, which stood at $28,913.


Why are the wages in the District’s retail sector going down? There could be multiple reasons for this. May be it is a reflection of what is going on in the entire economy: wages remained stagnant in all industries following the Great Recession, and perhaps retail was not immune to this trend. Data on wages earned in retail in the nation and the District shed some light: In 2012, the median hourly wage of retail workers across the nation, measured in today’s dollars, was $12.43, down almost 75 cents (or six percent) from five years earlier. Median hourly wages fell in the District too, but by even more: In 2013, median hourly wages were down to $14 from $16.4 in 2007 (in today’s dollars), or by about 15 percent. This suggests that additional factors might be depressing employee earnings in the District’s retail sector.

Are stores moving to part-time workers to reduce costs? We could find no evidence to support this. According to data from the American Communities Survey, the share of retail workers who work full-time has been stable, at 83 to 84 percent through the Great Recession and after.image010

Finally, it is possible that retail is paying less because people who work retail in the District are younger, less experienced, or less qualified in some other way. Data from American Communities Survey in the District reveals some interesting trends.  For example, in 2013, the average retail worker in the District was about 37 years old (not that different from the national average). That is three years younger than the average age in 2007, but it is hard to image that an additional three years of experience after mid-thirties would have such a big impact on wages.

As a side note: nearly half of District’s retail workers do not live in the District. In 2013, about 47 percent of retail workers in the District were District residents. 38 percent commuted from Maryland and 15 percent from Virginia. Incidentally, DC residents who work in retail are younger than their commuter colleagues at the age of 34 compared to 37 for those coming from MD and 44 for Virginians who work retail in the city. These shares and differences in ages are comparable to what we had seen in 2007.

Most retail workers are paid under $15 per hour–the focal point of the minimum wage debate in the nation and the District.  This makes us wonder: who earns low wages in the District and where do they live? What are some of their characteristics (age, work habits, educational attainment)? We are digging deeper into these questions and will post our findings soon.

What exactly is this data?

Employment numbers are from Bureau of Labor Statistics, Regional and State Employment and Unemployment (Monthly). The number of establishments refer to a store in a single location–that is stores that belong to a chain each count as a separate establishment.  This data come from Census Bureau’s annual Country Business Patterns. Gross receipts and payroll data come from the Economic Census,which recently released the 2012 Census data for the District of Columbia. The data on the full-time and part-time status of the retail workers in the District, as well as the age and residence of these workers come from the American Communities Survey’s 1-year PUMS data for the District, Maryland, and Virginia for the years 2013 and 2007.


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.





Residents move into the city for jobs, move out for housing

The District added about 90,000 net new residents between 2000 and 2014, but the population churn has been great. Current Population Survey data show that more than half a million people report moving to the District from some other state or jurisdiction during that period—this is on average 8 percent of the city’s resident population every year. Residents also move within the city frequently: In 2014, for example, nearly 60,000 residents moved houses within the city—this is approximately 9 percent of District’s resident population.


Jobs—or the prospect of one—is the top reason why the District receives new residents from other states. Between 2000 and 2014, the District received nearly 165,000 new residents because either they got a job in the District or their job was relocated in the District. Another 55,000 moved here to attend college, or they had just completed college, and found the District to be an attractive job market. Not all of these newcomers stayed, but it is interesting to see that over 42 percent of District residents who had moved to the city sometime in the previous 12-month period did so for their careers. Convenience of living in the city follows jobs, with another 10 percent of District residents suggesting that they moved into the city for an easier commute.


Why do people move out? It is housing. The top two reasons people report moving out the District in the last fifteen years have to do with wanting better housing, seeking cheaper housing, or wanting to own a house, for example, and these reasons account for 36 percent of the moves out of the District whereas they account for only 12 percent of the moves into the District. Jobs however, account for 12 percent of the people moving out compared to 32 percent moving in.


Incidentally, suburbs do a better job in attracting District residents than the other way around. Between 2000 and 2011, the District sent 391,000 of its residents to Maryland or Virginia (42 percent of those who left the city), but received only 191,000 new residents from these two states (30 percent of the residents who moved into the city). Looking at the reasons why people move to the suburbs, housing still plays a role, but the top reason is to establish a household. It appears from the data that those who share housing in the District with roommates are most likely to move out to the suburbs when they want a place of their own.


What exactly is this data?  Data on move rates  are extracts from the Current Population Survey data maintained by Miriam King, Steven Ruggles, J. Trent Alexander, Sarah Flood, Katie Genadek, Matthew B. Schroeder, Brandon Trampe, and Rebecca Vick. Integrated Public Use Microdata Series, Current Population Survey: Version 3.0. [Machine-readable database]. Minneapolis: University of Minnesota, 2010. The sample for the District is small–therefore the post looks at a combined 15 year period.

Note: an earlier version of this post was published before the draft was completed.

The changing face of volunteerism in the District: kids are the big winners

In Bowling Alone (2000), Robert Putnam claimed that civic participation in the U.S. has been declining. One metric he offered in his book was the decline in the number of volunteers in civic organizations such as religious groups, labor unions, Parent-Teacher Associations, or fraternity organizations.  Not everyone fully agrees with Putnam’s arguments: some suggested that perhaps civic participation  now takes different forms—not through PTAs, but perhaps through the Little League or soccer clubs, animal rescue operations, or other forms of volunteerism that did not exist before.

The District, with its changing demographics, is an interesting place to see how volunteerism has changed.  Do we see a decline in volunteerism as Putnam has suggested?  Or do we see a shift in the volunteer population towards new or different types of organizations and activities?  To answer these questions, we looked at data on volunteer activities for years of 2002 and 2013.  Although this is not the period Putnam is discussing in his book, it is the period where demographic changes were happening very fast in the District. Here is what we find:

  1. More people volunteer in the District, but each do so for fewer hours. In 2013, an estimated 150,000 District residents (or about 31 percent of District’s adult population) reported volunteering for a cause.  More people volunteered in 2013 compared to 2002 (up from 98,000), but each did so for fewer hours.  District residents who volunteer have been spending on average 130 hours each year on these activities—equivalent of three and a quarter weeks of full time work–down from approximately 163 hours or four weeks of full time work averaged between 2002 and 2005.  As a result, total volunteer hours increased by only 10 percent (from 18.8 million hours to 20.7 million hours) while the number of volunteers increased by 50 percent.image001
  2. Women’s contribution to volunteer activities is declining in the District. In 2002, women collectively contributed 13.3 million hours of volunteer work; in 2013, their total contribution had declined to 12.4 million hours.  Women volunteer more frequently than men do, and generally put in more hours.  In 2013, women provided approximately 60 percent of all volunteer hours in the District, providing, on average 21 hours more of volunteer hours compared to the District’s men who volunteer.  In 2002, women provided 71 percent of all volunteer hours and provided, on average 88 hours more of service compared to the men.  During the same period, the share of women’s contribution to total hours volunteered fluctuated between 56 percent and 58 percent across the entire nation.image002
  3. The growth in the number volunteers between 2002 and 2013 entirely came from the increasing white population, but the growth in the total number of volunteer hours came entirely from the African American community. African American residents of the District are less likely to volunteer; however, when they do, they volunteer twice the time at 226 hours per year (or over 5.5 full weeks of their time) compared to their white counterparts.image003image004
  4. District residents with higher levels of education are more likely to volunteer, but they do so for fewer hours. Half the residents with a professional and doctoral degree volunteer for some cause, compared to 10 percent of the population with a high school degree, but they only commit about 78 hours per year—this is one third of the hours committed by volunteering adults (the data track those 15 and older)  who have a high school degree only.
  5. Types of organizations that attract volunteers changed significantly. Looking at the type of organizations people volunteer for (here we are tracking organizations people report as their top three volunteering outlets), we see some interesting stories. The table below tracks both how these organizations are growing (second column) and how the District’s volunteer population is shifting across them (third column).  This last metric shows us what types of organizations are gaining relative ground, which ones are stable, and which ones are on the demise.
    • Kids are the big winners. Between 2003 and 2013, volunteer organizations for kids’ sports and rec groups (the Little League, soccer groups, chess clubs etc.) have added 19,320 volunteers going up from 25,000 volunteers to 44,400 (or 77 percent growth over its base).  This was already big in 2003, but became even bigger, with 20 percent of the volunteers engaged in these organizations in 2013 compared to 17 percent in 2003.
    • Volunteer definitions are changing. In 2003, only 3,000 adults, or two percent of District’s volunteer population, reported volunteering for an organization that did not fit under any of the traditional types of organizations. This number grew by 14,000 and now stands at 8 percent. What types of organizations might these be? Perhaps they are  technology focused organizations (Engineers Without Borders, Wikipedia, the Guttenberg Project, or open source foundations such as Yorba (though they have been denied nonprofit status by the IRS). image001
    • Environmentalism is on the rise; immigrant and refugee assistance is up and coming. Between 2003 and 2013, the number of DC residents who identify environmental or animal care organizations as their volunteering outlet has increased by 5,800; or by 120 percent. They still constitute a small, but rapidly growing, share of volunteers.  Volunteerism for immigrant and refugee assistance groups did not even exist in 2003 (a surprise, given the international nature of our city); now they attract 1,332 residents.
    • Public safety is dead. Not a single resident reported volunteering for a public safety organization in 2013. Their numbers were already low in 2003, but now we see that volunteering for the fire department or neighborhood watches are things of the past. We have completely professionalized this area.
    • Religious organizations and social and community service groups are still big and growing, but not as fast. Social and community service groups added nearly 9,900 volunteers, but their growth lagged, at 28 percent, behind the city average of 50 percent. These type of organizations now attract 20 percent of all volunteer adults, as opposed to 23 percent in 2003.
    •  Here is what the newcomers to the District (read, millennials) do not care for: Political parties and advocacy organizations, cultural and arts organizations, sports and hobby groups, labor unions, professional organizations and health research and education groups. These kinds of organizations either lost volunteers or barely added any new ones.
  6. Types of volunteer activities are changing too. The table below, which tracks people’s main volunteering activity, tells us the following:
    • Counseling, music or artistic performances, and tutoring and teaching are on the demise. Counseling lost half of its volunteer base, music or artistic performances lost 40 percent.  With counseling, it might be that people are reluctant to offer this service without proper credentials.  Tutoring and teaching is still a large activity (28,500 residents report it as their main activity in 2013) but this base did not grow at all from 2005.
    • Coaching sports increased its base by 50 percent, going from 5,100 adults reporting this as their main activity to 7,675. This is just another measure of the growing importance of kids.
    • The real surprise here is usher and greet activities. This group added 7,753 new volunteers since 2013. Given these activities generally attract older people–the median age among these volunteers is around 50, compared to 38 for all those who volunteer–it is odd to see such growth in a city that is flooded by young people.  However, as we have noted elsewhere, the senior population in the District has been growing, both in numbers and riches, perhaps increased volunteerism among this group should be expected.

image010 What is the takeaway from all of these?  First, volunteerism is still growing in the District, but our time commitment to volunteer activities is on the decline.   Second we are more focused on our children, and to some extent, on our ideals.  Work related volunteerism is on the demise; so is volunteerism in health and social services sectors, perhaps because of credentialing requirements.   Finally, an increasing share of volunteers work for organizations that are not in the traditional definition of volunteering outlets. It would be interesting to find out what they do.