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.

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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.

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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.

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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.

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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?

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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.

 

$26 Billion of Taxation without Representation

Annual Federal Tax Payment

A recent court decision gave the District of Columbia control over its local budget for the first time since home rule in 1972. At a time when support for D.C. statehood is at an all-time high (67 percent of residents are in favor ), the court’s decision has brought a renewed optimism to the District’s 51st state movement. The Mayor and Council are taking advantage of the momentum generated by the court decision to propose a new ballot initiative and legislation to further the statehood agenda.

We all know the District’s statehood battle cry – Taxation Without Representation – but how much do we actually pay to the federal government each year? In order to answer this question, we dug into the federal tax collections data. What we found is that District residents and businesses paid a whopping $256 billion in federal taxes since the IRS began to track the District’s tax collections separately from Maryland in 2002. In 2014 the District paid the federal government $26.4 billion which is likely the largest contribution in the city’s history. The District only received $3.5 billion in federal grants, payments, and court contributions in fiscal year 2014 – a difference of roughly $22.9 billion (this excludes matching federal funds such as those we receive for Medicare, but all states receive these). Since 2002, the annual tax paid to the federal government grew by $11.7 billion (an increase of 80%).

Annual Federal Tax Payment

In 2014 the District paid more federal taxes than 22 states and paid nearly the same amount as South Dakota, Alaska, Montana, Wyoming, and Vermont combined. Those states have 15 seats in Congress while the District has only one non-voting delegate.

Annual Collections Rank

Although some states pay more federal taxes than others, it does not necessarily translate into more congressional representation. In 2014, New Jersey paid $9.63 billion of federal taxes for each of its 14 congressional seats – the highest in the country. West Virginia on the other hand only paid $1.38 billion for each of its five congressional seats – the lowest in the country. If the District became the 51st state in 2014 and had three seats in congress, it would have paid the fourth highest amount of federal taxes per congressional seat.

Federal Taxes Paid for Each Seat

What exactly is this data?

The IRS Data Book is published annually by the IRS and contains statistical tables and organizational information on a fiscal year basis. The data used in this analysis is published by the IRS annually and is available here. We used data from the Comprehensive Annual Financial Report for 2014 to obtain total revenue and federal government contributions to the District. In order to calculate federal spending on the District’s judicial system, we collected information from the District of Columbia Budget Request Act and the Budget of the United States Government for 2014.

Have you ever moved without getting your utility deposit back? Still have those uncashed checks? Or, have you forgotten about an old checking or savings account?

According to the National Association of Unclaimed Property Administrators (NAUPA), states are collectively holding on to approximately $41.7 billion in unclaimed assets, including dormant bank accounts, stock splits, life insurance payouts, gift cards and uncashed payroll checks among other funds. This is up 27% from $32.8 billion in 2010.

Consistent with the nationwide trend, unclaimed monies in the District have shown a modest increase over the course of the last 10 years. As of the end of FY 2015, total unclaimed funds in the District amounted to $300 million corresponding to approximately 1.4 million account records. The proportion of claims paid out to rightful owners has also picked up since FY 2012. Below is a chart depicting this trend.

Unclaimed Property Remittances Vs Claim Payouts

If you haven’t heard of unclaimed properties before, here is a simple description: if banks, insurance companies, credit unions, pensions and similar entities owe you money or other financial assets and you do not collect it, then it’s called unclaimed. These assets could be checking accounts, certificates of deposit, customer deposits and over-payments, gift certificates, paid-up life insurance policies, unpaid wages, insurance payments, money orders, refunds, saving accounts, proceeds of safety deposit box auctions, etc.

By law, all these entities have an obligation to remit the unclaimed property in their possession to the appropriate state(s). The idea is that the property is not theirs, and that states have more resources for finding the individual or company that has ownership. However, if no owner is found, states get to keep the property. In the District, all funds received by the Unclaimed Property Unit are deposited into the District’s General Fund. Although the money belongs to the owners, who may claim it at any time, the funds may be used in the interim to help defray the District’s operating costs.

Below are few statistical highlights that we thought would help increase public awareness of unclaimed properties in DC and nationwide.

  • In FY 2011 (also see the chart above), a record $22 million was returned to the rightful owners by the District. This was the largest total claim payment ever in a fiscal year.
  • The largest claim ever paid to an individual also occurred in FY 2011, about $1.2 million. This may also be an effect of the last recession, as people tend to look for missing monies during economic hard times.
  • The number of claims in the District has increased by 89% during the last 10 years whereas the number of accounts remitted rose by well over 100%.

Unclaimed Property Trend1

In the District, uncashed checks and forgotten account balances seem to constitute the largest sum of unclaimed properties, whereas nationwide, orphaned 401(k) accounts (401 accounts forgotten by employees after they leave their job) are reported to bring in the most.

Unclaimed Property by Type

  • In a nationwide survey done in the year 2014, the State of New York topped the list with $12 billion unclaimed funds reported statewide. Our neighboring state Virginia stood 10th during that year. Below is a table showing where DC and neighboring states stand as of the end of FY 2015.
States/City

DC

MD VA

NY

Estimated amount of Unclaimed Funds as of the end of FY 2015 (in millions)

$300

$838 $1,200

$14,000

So, if you think you might be missing money, you may start your search with www.Unclaimed.org or http://www.missingmoney.com which would direct you to the appropriate state unclaimed property website.

 What exactly is this data? The above analysis is based on historical Unclaimed Property Remittance and Claim Payment data obtained from the District’s Office of Finance and Treasury-Unclaimed Property Division. States have different unclaimed property laws that dictate how they are reported and accounted for. The FY 2015 Unclaimed Property fund balance data was obtained from each state’s Unclaimed Property Offices. The District’s unclaimed property laws are available at D.C. Code § 41-101 and § 42-201. The nationwide data was obtained from newsletters published by NAUPA and also available at www.Unclaimed.org.

 

The Increase in Higher-Income Children in D.C.

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Recently we reported that the total number of children in D.C. has been increasing since 2010 after about fifteen years of decline. Since 2008 the increase has been driven by young children under 6, and in 2012 the number of school-aged children (aged 6 to 17 years) began to increase as well.

Now we look at how the number of children in different income groups has been changing. We define lower-income as households making less than $50,000; middle-income as households with incomes between $50,000 – $150,000; and higher-income as households making over $150,000

Both higher-income households and lower-income households are driving the boom in young children under 6. The average number of children in both income groups increased between the two five-year periods for which we have reliable data: 2006 to 2010 and 2010 to 2014. The higher-income group, though, grew more.

A different story emerges for school-aged children. For children between the ages of 6 and 11, there was a net increase only among higher-income kids. The number of lower-income and middle-income children in this age group declined slightly, but because the declines are small and the data has a sampling error, we are less confident in these trends. (We discuss the reliability of the data at the end of the post.)

For older children between the ages of 12 and 17, the average number of lower-income kids declined between the two five-year periods that ended in 2010 and 2014. The data shows a slight increase in the average number of higher-income children and a slight decrease in the average-number of middle-income children, but we have less confidence in these trends since the changes are small.

Lower-income children still outnumber middle- and higher-income children of all ages. But the higher-income group appears to be catching up with the middle-income group, especially among younger children.

Is this proof of gentrification? The general trends suggest yes. When we look at children of all ages, only the higher-income group has, on net, added children. The total number of lower-income and middle-income children has remained about the same between the two five-year periods that ended in 2010 and 2014. This means that higher-income children now make up a bigger portion of all children in the city. But it could be that because we’re looking at five-year groupings of data, we’re missing year-to-year trends that could show more nuanced trajectories for children in different income groups. It’s quite possible, for instance, that the number of school-aged lower-income children has been increasing the past few years, but losses in the late 2000’s and early 2010’s have outweighed recent gains. Or it could be that the number of school-aged lower-income children has not begun to increase yet, but will soon, following a trajectory similar to that of higher-income children.

What exactly is this data?

Our data comes from the American Community Survey (ACS) 5-year data sets for 2006-2010 and 2010-2014 for the District of Columbia. We could not use ACS 1-year data sets because the small sample size makes them unreliable for this analysis.

Errors for each of the data points in our graph range from +/- 6% to +/- 11% for 90% confidence intervals. For the bolded lines in our graph, trends (population loss or decrease) still hold even if we assume the largest errors (at 90% CI) in the least favorable direction. For the non-bolded lines, trends reverse when we factor in the largest errors in the least favorable direction.

Household incomes are in 2014 dollars.

 

Local government, food services, and retail contributed more to 2015 job growth in DC according to revised estimates; business services and health contributed much less

Every year in March the US Bureau of Labor Statistics (BLS) revises state employment data, in this case from March 2014, based on more complete information. This year’s result is a net reduction of 3,066 jobs in DC—0.4% of all jobs in the city—for the December quarter of 2015 compared to the previous estimate that had been made in January.

This reduction was the consequence of changes in the various sectors of the economy. Private sector jobs were decreased by 5,566 jobs (1.0%), for example, while public sector ones were marked up by 2,500 (1.1%).

Revisions to some individual sectors were of sufficient magnitude to change how important they were in the District of Columbia labor market in 2015. Higher estimates for local government, food service, and retail gave these sectors leading roles in DC’s job growth last year. Conversely, health and business services were found to have played much smaller roles.

For the economy as a whole, the BLS revision reduced the rate of job growth from the December quarter of 2014 to the December quarter of 2015 from 1.3% to 1.1%. The revision reduced private sector growth from 1.8% to 1.3%, while the public sector went from virtually no growth to 0.9%.

In terms of employment, the revision makes DC a 30.9% government town rather than a 30.4% one.

Gaining sectors

The upward revision in public sector jobs is entirely attributable to local government—an increase of 2,767 (7.5%). (The federal government was trimmed by 267.) In the previous estimate local government jobs declined from 2014 to 2015; with the revision they go up by 1,433, equivalent to 16% of all job gains in DC for the year. The revision seems consistent with recent DC budget increases.

Food service was previously estimated to have added 367 jobs over the past year, 4% of all city job growth. The newly estimated yearly increase is 2,600—30% of all DC job growth, second only to professional and technical services. Similarly, the retail sector’s share of all one-year DC job growth has been raised to 19% from the previous estimate of 3%.

Although education got 1,400 more jobs in the revision, this was not enough to increase jobs in the sector over the prior year. The new level in 2015 is still estimated to be 1,900 (2.9%) less than a year earlier.

Losing sectors

Four sectors drove the downward revision of private sector jobs—health, business services, non-profit organizations, and finance. Together they lost 12,301 jobs. Business services declined 7.9%, health, 6.3%.  In the previous estimate, these two sectors, with 16% of all DC jobs, added 7,200 jobs in the past year, accounting for 75% of then estimated one year job growth in the city. The new estimate shows one year growth of only 900 jobs for the two sectors, a modest 10% of all DC job growth.

revision 3 group

revised growth 2

What exactly is this data? Each month the US Bureau of Labor Statistics compiles wage and salary data by industry for states and metropolitan areas.  In March of each year BLS revises the information for at least the previous year.  This blog compares the previous estimates through December 2015 that were issued in January 2016 with the revised ones issued in March 2016.  The December quarter is the average of October, November, and December.  The data are not seasonally adjusted.

A version of this blog appeared in the February 2016 District of Columbia Economic and Revenue Trends report. The Trend report also compares revisions for DC with those of the Washington Metropolitan area as a whole, and has a table showing revisions for all sectors of the economy.

 

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.

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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.

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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.

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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.

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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.

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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.

 

How can the rent be so high in DC when almost two-thirds of all rental units in the District are subject to rent control? A small number of “spoiler “units with high turnover may be the reason.

The District, along with a few other housing major markets in the nation, has rent control laws that were enacted to protect tenants against unreasonable rent increases. The laws governing rent control in these markets generally stipulate that rent increases are bound by the Consumer Price Index or other cost of living measures. These laws also allow for larger increases when units become vacant (we will return to this point later).

If rent increases are generally constrained by the cost of living, how could the median rent in the District over the 2005-2014 period have grown by 65 percent, or nearly 6 percent annually, when the DC consumer price index over the same period grew by only 30 percent, or just under 3 percent per year? In addition, as shown below, growth in median rents in DC has outpaced other markets where rent control laws are also in place.

Median Contract Rent: 2005-2014

1

Source: U.S Census Bureau, American Community Survey, DISTRICTMEASURED.COM

One possibility is that most units are not subject to rent control and must pay what the market will bear. To analyze this, we looked at data on the number of rental units subject to rent control in the District. Under DC laws most rental units with more than 5 units and built prior to 1975 are subject to rent control.

Here’s what we found in terms of the number of units subject to rent control

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Source DC Office of Tax and Revenue, DC ORA, DISTRICTMEASURED.COM

This is what the data looks like by Ward

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Source: DC Office of Tax and Revenue, DC ORA, DISTRICTMEASURED.COM

Highlights

  • Almost two thirds of all rental units are potentially subject to rent control or other restrictions. This is a significant share of the entire rental stock.
  • The highest concentration of rent control units was located in Wards 3 and 4 with over 80 percent of all units potentially subject to rent control.
  • Wards 5, 6 and 7 had the lowest overall share of rent controlled units. In Ward 6, less than a third of all units are subject to rent control.
  • These overall findings are largely consistent with the results of a prior study from the Urban Institute, “A Rent Control Report for the District of Columbia” by Peter A. Tatian, Ashley Williams, June 17, 2011 which can be accessed by clicking here

 

To put these statistics further into perspective we looked at the share of rent regulated apartments in New York City.

This is what we found for New York City’s Boroughs

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 Source: 2014 New York City Housing Vacancy Survey, U.S. Census Bureau

DC ‘s overall share of rent regulated units is comparable to New York City’s, although certain DC Wards have an even higher share of regulated units than in the Bronx or Manhattan.

So what accounts for the large increases in rents given that two thirds of DC rental units are subject to rent control?

Two provisions of the law are likely to account for this:

When a DC tenant vacates a rental unit the amount of rent charged may, at the election of the housing provider, be increased:

(1) By 10% of the current allowable amount of rent charged for the vacant unit; or

(2) To the amount of rent charged for a substantially identical rental unit in the same housing accommodation; provided that the increase shall not exceed 30% of the current rent charged for the vacant unit.

It is easy to see how the combination of these two provisions can result in substantial price increases. This is what could happen to rents in a building where one unit (Unit 1) has a relatively high turnover. We assumed a turnover of once every three years for this unit, which is not unusual given DC’s high mobility. The other units have no or limited turnover. We assumed all comparable units started with a rent of $1,000 in 2004.

Rent simulation given turnover

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Source: DC Office of Revenue Analysis, Cells highlighted in orange indicate a vacancy rent increase of 10%, and red denotes up to a 30% percent increase based on a comparable unit

Turnover in one “spoiler” unit can cause rents to increase for all comparable units in the building.

Even the third unit in this hypothetical building, which turns over only once in ten years, has seen its potential rent increase by 58 percent. Only the fourth unit, that has seen no turnover, has a rent that remains below $1500.

As shown above, given allowable vacancy increases and comparability under DC law, even one comparable unit with a high turnover can cause rents to increase substantially for many units. Higher turnover, which may be due to changing demographics (more married couples and fewer singles remaining in the same unit for many years) or a spoiler unit, which may be the substantially identical unit on the same floor but close to the garbage room or near the garage exit, could cause rents to increase significantly even with rent control.

What exactly is the data?

To determine units potentially subject to rent control we looked at the year built and number of units for the following building codes for rental properties (21, 22, 25, 28,216 and 217) from the DC Office of Tax and Revenue Real Property Tax Database. The New York City Housing and Vacancy Survey (NYCHVS), sponsored by the New York City Department of Housing Preservation and Development, is conducted every 3 years to comply with New York state and New York City’s rent regulation laws. The Census Bureau has conducted the survey for the City since 1965. The 2014 NYCHVS is the 16th such survey. No similar study exists for DC.

Bob Zuraski contributed to this post