The Mystery of the District’s Self-Employed

When you have eliminated the impossible, whatever remains, no matter how improbable, must be the truth.

-Sherlock Holmes

When the CARES Act passed in March, self-employed persons were granted eligibility to apply for and receive unemployment benefits for the first time in history through the Pandemic Unemployment Assistance (PUA) program.  The self-employed have always been a part of the economy, but the CARES Act marked a turning point for this business type with formal acknowledgement by legislators to the importance of supporting them. But who are the “self-employed”? The identity of the self-employed has long been a mystery. And like the solution to any mystery, one must ask the right questions, find the right clues, and piece together those clues to arrive at the truth.

With the self-employed people two narratives exist. In some cases, self-employed data is lumped in with businesses, and in others they are an employee for themselves. Even among policymakers, the overarching question of, “who and how many are self-employed?” is often debated. Are the self-employed only those people in the “gig economy” or are they established firms that we may frequent daily without knowing they are self-employed? To solve this mystery, we dug through publicly available federal data from the Census for the District of Columbia to gain a better perspective on this significant group. For the sake of simplicity, the self-employed referenced here are “single-person firms” where the owner is the only employee of the firm.

The Tale of the Self-Employed Establishment
From the business perspective, how many businesses within the District are considered one-person self-employed organizations? Using the Statistics on U.S. Businesses and Non-Employer Statistics Data from the Census for 2007 through 2017, Figure 1 reports that in the past ten years self-employed establishments have grown to account for 70 percent of all establishments in the District. Over this same period, the self-employed share of total establishments grew by 4.1 percentage points from 65.9 percent to 70 percent in 2007 and 2017, respectively.

Figure 1: Share of Establishments by Type, 2007 vs. 2017

Source: Census, Statistics of U.S. Businesses and Non-Employer Statistics

As of 2017, 54,965 establishments within the District were registered as self-employed. While these establishments make a significant impact in terms of the number of establishments, their contribution to District total wages and salaries is limited. Traditional businesses with an owner and employees will often budget anywhere from 15 to 30 percent of sales for payroll. Assuming an average of 22.5 percent for payroll as a share of revenues, ORA estimated from sales receipts data the payroll size of self-employed versus employer establishments and compared their contribution to total District payroll.

From the Employee Narrative
In the self-employed establishment, payroll expense varies depending on the share after expenses remaining. As noted in Figure 2, the self-employed account for approximately 1.0% of all payroll earned within the District between 2007 and 2017.

Figure 2: Share of Payroll by Establishment Type, 2007 vs. 2017 (in billions)

Source: Census, Statistics of U.S. Businesses and Non-Employer Statistics, ORA

The share of payroll accounting for those self-employed as employees modestly grew to 1.1% in 10 years, but overall remains marginal compared to the traditional employer establishments. Despite the total establishments that are self-employed, the core fact remains that they are still an employee and when thinking about payroll, this would only account for 60,000 potential employees within the District. Whereas employer establishments employed 527,004 employees according to the data in 2017. Thus, while the self-employed make a significant share of total establishments, as individual employees they are vastly outnumbered by those in traditional employment in the District.   

The Red Herring of the Self-employed and the App-service Employment
Contrary to popular belief, self-employed individuals are not relegated to only ridesharing or other app service employment. As Figure 3 shows, the majority of the self-employed are to be found in the Professional, scientific and technical services sector, one of the District’s largest employment sectors, and a major driver of District economic growth in recent years.

Figure 3: Top 10 Industries for Self-Employed (Total Establishments), 2017

Source: Census, Non-Employer Statistics

According to the Census data, in 2017 the total sales for self-employed establishments was $1.9 billion. In context, that was the going rate if you wanted to buy both the Tampa Bay Rays ($825 million) and Miami Marlins ($940 million) in 2017.. Looking back at Figure 3, Professional, scientific, and technical services accounted for 30 percent of the total self-employed establishments. Jobs within this sector include consultants, lawyers, and computer programmers. In terms of sales, the sector accounted for nearly 43 percent, or $821 million, of the total self-employed sales that same year. Real estate and rental and leasing sector, which includes real estate agents and property management companies, although a smaller share of total establishments accounted for an estimated 10 percent of the total sales in 2017. Combined with Professional, scientific, and technical services, the two sectors account for nearly 35 percent of the total establishments, and 53 percent of all self-employed establishment sales in 2017 or $1.0 billion. While many self-employed could be from ride-sharing, the data indicates a larger portion may be in more lucrative industries.

Concluding Remarks
The mystery of the self-employed has eluded policymakers for decades. Dueling narratives of self-employed as establishments or employees have complicated the issue even further. As establishments, the level of newly self-employed within the District has grown continuously since 2007 to become 70 percent of all establishments by 2017. Viewed as  employees alone underestimates the significance of self-employed as an important driver of District economic growth, given the share payroll in 2017 accounted for by self-employed was estimated to be 1%. Contrary to popular thought, the self-employed are not restricted to retail or transportation, but are prominent in well-established skill-based sectors of the District economy. Overall, their contributions are not limited to tip-based income but have amassed to $1.9 billion in sales annually. However, there is no easy solution to the mystery of the self-employed, only more questions. But, the Census data may be the cipher we need to begin unraveling this mystery and answering those questions in the future.

For an interactive experience, check out our blog dashboard companion piece in Tableau, “The Mystery of the District’s Self-Employed”

Using high-frequency data for real-time tracking of the economic impact of COVID-19 on the District of Columbia

The COVID-19 pandemic has wreaked havoc on US national, state, and local economies. One of the difficulties in assessing the extent of the economic impact is the lack of timely data. While measures to control the pandemic rapidly destroyed jobs in the retail and hospitality and leisure sectors, official US Bureau of Labor Statistics figures on jobs at the national level were not available until a week after the end of the month when the jobs were lost.  And at the state and local level, the jobs numbers are available two are more weeks after the end of the month.  Lag times for personal income and gross domestic product (GDP) measures are even longer. Initial estimates of national GDP are not available until a month after the end of a quarter, and it is even longer for state and local governments. For example, the US Bureau of Economic Analysis (BEA) published 1st quarter personal income figures for states and the District of Columbia on June 23, almost three months after the end of the quarter, and first quarter GDP figures for states and the District will not be available until July 30th.

To track the unfolding impact of the pandemic, we have used various high-frequency data, including daily and weekly unemployment claims, smartphone counts in the District of Columbia, and credit card data on expenditures. Below we described the trends for the smartphone counts and credit card spending before and after the start of the lockdown to control the pandemic.


Daytime District population, which includes commuters and tourists and almost doubles the resident population, drives the economic activity in the District. So, tracking the impact of the pandemic on daytime population is a key measure of the pandemic’s impact on DC’s economy. Figure 1 below shows the smartphone counts in the District from January 2017 through June 2020. With schools and businesses closed, DC saw its lowest daytime population numbers, by a wide margin, since the data was first collected in January 2017. The data, provided to the District by Thasos, shows a 69% drop in the number of people in DC compared to the same period the year prior (April 1-June 1). To put that in perspective, the drop from the historically highest average week for DC (first week in April AKA spring break), to the lowest average week (winter holidays/New Year’s Day), is 37%.

Figure 1: DC Average Daytime Population by Week

daytime pop

Tableau Link

When looking at specific days, blizzards and Christmas in DC have traditionally been the lowest days at 430k-450k daytime population, and the COVID-19 lockdowns have seen days in the 300k range.

Note: The data received by the District is one aggregate number per day and is therefore completely anonymized. The data pulls from a geofence that is aimed at capturing commuters and while it captures most of the city, it does not account for every person in DC every day.

Credit Card Data

Credit card spending data from Earnest Research, which is aggregated and stripped of any personal identification information, give a real-time view of how spending has declined during COVID-19. As Figure 2 shows, in DC total spending has fallen by about 30% since April 15, 2020 compared to the same period 1 year before.

Figure 2: DC Credit Card Spending, All Categories

DC All Spending

Tableau Link

As Figures 3-5 shows, some sectors had larger declines than others. Two of the hardest hit sectors are restaurants and hotels. Figure 3 shows that restaurant spending for the week of June 17 is 46% below the same period last year, while Figure 4 shows that hotel spending is almost 90% below the same period last year. On the other hand, Figure 5 shows that grocery spending soared at the beginning of the lockdown, rising as high as 45% in the first week of April compared to the same period previous year. Grocery spending has gradually fallen back to just above pre-pandemic lockdown levels.

Figure 3: DC Credit Card Spending, Restaurants

dc restaurant spending

Tableau Link

Figure 4: DC Credit Card Spending, Lodging

dc hotel spending

Tableau Link

Figure 5: DC Credit Card Spending, Grocery

dc grocery spending

Tableau Link

DC Spending Relative to US and other Jurisdictions

National credit card spending data has basically recovered to what it was in 2019. Cities are experiencing much larger declines due to loss of commuters and having a different mix of businesses compared to suburbs/rural areas. Figure 6 shows DC compared to a select group of cities. NYC has seen the largest total spending decline of any large city with a ~50% drop. DC has been down approximately 30% and Los Angeles down ~10% during this period.

Figure 6: DC Credit Card Spending Compared to Other Jurisdictions

dc compared

Tableau Link

$15 Minimum Wage and the Earned Income Tax Credit: Public Policy Interactions

On July 1, 2020, the minimum wage in the District of Columbia will be $15 per hour. Our  recently published study in the Economic Development Quarterly, finds that a $15 minimum wage will produce significant income gains for most of the District of Columbia’s 61,000 low-wage resident workers and only slightly impact the city’s level of relatively low-wage jobs (see here and here). The study also finds that in 2021 approximately 62 percent of the resident workers impacted by the city’s $15 minimum wage policy ($15 MWP) will consequently lose a sizable amount of their Earned Income Tax Credit (EITC). This policy-induced increase in wage income, however, is estimated to more than offset the amount lost in EITC for this subpopulation of resident workers.


The Income Effects of the $15 MWP on the Workers that Claim the EITC

The federal EITC is based on a schedule where many EITC recipients earn a decreasing EITC amount as their annual income increases. The policy simulation models we used in this study indicate that most of the resident workers in this analysis are expected to lose some federal and DC EITC dollars as a direct result of the $15 MWP. Figure 2 shows that the average full-time worker in 2021 will gain $3,097 in higher annual wage income.  (Year 2021 is one year after the full implementation of the $15 MWP and when the national & local economies are estimated to be recovering from the current pandemic and recession.) However, the higher income from the $15 MWP will cause the worker to lose an average of $332 in combined federal and DC EITC, causing the average worker (with 1 EITC dependent) to be better off with a net increase in resources amounting to $2,765 or 11.3 percent (Figure 3). In contrast, full-time workers that do not have dependents and are not EITC recipients are likely to be only $2,587 or 10.5 percent better off (Figures 4 and 5). The interplay between the $15 MWP and the EITC is such that the consequent lower federal and DC EITC amounts for many under the $15 MWP are more than offset by the policy-induced wage rate increase.




On July 1, 2020, the minimum wage in the District of Columbia will increase to $15 per hour, and every year thereafter the wage will be increased in tandem with the area’s inflation rate. Via the policy interaction of the $15 MWP and the EITC, we find that there is a substantial overlap of subpopulations of DC resident workers from each policy. We estimate that of the 61,000 resident workers impacted by the $15 MWP, 38,000 of them will lose $16.4 million in federal and local EITC payments in 2021 in exchange for $54.6 million in higher wage income.

The economic burden of the pandemic is currently being borne mainly by low-wage workers in the hospitality and retail sectors. As the economy reopens and these workers are re-employed, the planned increase in the minimum wage will help in 2021 to reverse some of the economic setbacks to low-wage workers in the sectors most affected by the pandemic.

What are the sources of revenue for the District’s Government? DC Tax Facts, A Visual Guide, Part 1

This blog series is built on a report that analyzed annual tax revenues for the District’s Government.

In 1871, (before District residents had the right to self-government in the Home Rule Act of 1973) District legislators officially adopted taxes as the major engine to balance the District’s expanding annual budgets.[1] Questioning why they choose to tax real estate or why it is taxed the way it is (using the value of land and improvements or buildings, as compared to just the land value), and debating its fairness or economic efficiency is for policy makers to decide and not the intention of this discussion.[2] In this blog post and in later installments, we will study what are the sources of District Government revenues and how they are collected using visually aggregated statistics with our latest tax data.

To start, almost 65 percent of the District Government’s revenues came from its own taxes on various transactions, possessions, profits, goods, and services within its borders in Fiscal Year (FY) 2018. There are three other primary sources of revenue the District Government uses for its day-to-day operations and to pay for capital improvement projects. These other sources are the non-tax revenues generated by the District Government, Federal funding, and private grants or donations. Figure 1 represents all of the revenues in FY 2018 gathered by the District Government from the four sources of funding. The General Fund, which functions as a large pool of money, includes all the local revenues raised by the District Government and is comprised of taxes, fines, fees, charges, economic interests, and bond proceeds or bond administration fees. These local revenues are usually separated from Federal and private revenues in the city’s budget books. Later in this post, we describe the four main sources of revenues, where they come from, and how they are distributed among District programs and activities.

Figure 1: All the District Government’s Revenue in FY18 [3]

Local Revenue

The first and most important source of funding for the city’s balance sheet is the local revenue we generate for ourselves. These local revenues represent 74.3 percent of the total revenue collected in FY 2018. Figure 2 represents a snapshot of the different kinds of FY18 District local revenue.

Figure 2: Local Revenues Collected in FY18

Within the General Fund there are three appropriated or devoted funds: The Local fund, Dedicated Taxes fund, and Special Purpose (or O-Type or Other) Revenues fund. And there are two ways in which revenue raised from the District is distributed to agencies or governmental activities like education, health and social service, criminal justice, etc.:

  • Through the Local fund all the tax and non-tax revenues not legislatively restricted for a particular purpose are designated for each agency or activity through the city’s annual budget process. To access full reports on recent approved budgets please visit
  • Or, through a legislatively required investment or reimbursement to an agency for a particular purpose, e.g. dedicated taxes and special purpose revenue. Dedicated tax revenue is transferred out of the Local fund and is not available for general budgeting. For example, a small percentage of the sales and use tax is dedicated to the Healthy Schools program every year. Special Purpose Revenue, i.e. O-Type or Other revenues, comes from non-tax revenues generated from fees, licenses, permits, etc. that are dedicated back to the specific agency performing the fee-based function.  For example, Capital Bikeshare’s usage fees go to the Department of Transportation’s Bicycle Sharing fund which covers the city’s share of the service’s maintenance and operation.

The District Government relies on three local taxes, income, property and sales and use, that flow into the General Fund, as shown in Figure 2. The individual income tax is the District’s main revenue source and it comprises 23.1 percent of all local revenues. The property tax is the second making up 18.7 percent of the Local fund. Taxes on commercial properties bring in most of the property tax revenue, partly because commercial property is taxed at a higher rate than residential property and the market values of commercially zoned buildings are much higher in comparison. And, the District’s sales and excise tax comprises about 17.9 percent, the third largest source, of the Local fund. In FY18, the District raised $8.9 billion in taxes — equal to over six percent of the District’s economy that produced $142.626 billion in gross domestic product in calendar year 2018.[4]

The inner pie chart of Figure 2 shows the major types of taxes/non-tax revenue as a percent of the total amount collected in the middle ring. The outer part of the pie chart breaks down each major tax and non-tax by source with the percentage that each source contributes to the total revenue collected on the outside.

Federal Revenue

Federal grants come directly from Federal agencies to District agencies to fund certain specified services or functions that the District as a state, county, city and school district manages, such as road repair, education, community development, among other activities. These grants come in various forms like block grants (funding for broad purpose programs like community development and provide more spending autonomy to District agencies); formula grants (formulas established by law that fund state-administered Federal programs such as Medicaid or TANF with most funding formulas determined by a state’s population, median income, poverty, etc.); certain entitlements (binding obligations by Congress  to pay for state-administered programs like Social Security); and competitive grants (grants that District agencies apply for and compete against other potential recipients who also meet a grant’s eligibility requirements). This means the amount of Federal funding from year-to-year does not stay the same.

Even though the Federal Government provides grants and payments to all 50 states plus their localities and U.S. territories, through the Federal Government’s own income tax revenue, the District has had a unique funding relationship with the national government. Due to structural budgetary issues like large amounts of tax-exempt properties, the costs of public events, such as Presidential inaugurations and national demonstrations, as well as financial mismanagement and other mitigating circumstances, the District’s Government came under the supervision of the Congress’ Financial Control Board in the mid-1990s. Through various agreements with Congress, the District relinquished financial responsibility over its public employee pension system before 1984, its prison and court systems, and received a higher share of Medicaid payments among others.

All the Federal money and private grants or donations go into the separate Federal and Private Resources Fund. Part of the reason for this is that when the District receives money from Federal sources it comes with conditions, guidelines or requirements, and cannot be indiscriminately spent on activities. There are, however, a few exceptions to fund placement, like Federal funding for capital projects going directly to the General Capital Improvements fund, or instances where a fraction of Federal funding is transferred to the General Fund to pay for the indirect costs an agency incurs while managing a Federal grant.

The Federal funds the District receives are grants or payments that vary in size, frequency, and intent. In FY18, Federal contributions or payments and operating grants, including private sources, accounted for roughly 26 percent of the District’s total annual budget.[5]

Federal payments are direct appropriations from Congress, usually to a District agency for a particular purpose like security or logistics for public events like state funerals, Presidential inaugurations and demonstrations; they are like formula grants but are not mandatory or based on certain indicators. Another example of a Federal payment would be for Medicaid and Medicare because they are Federal programs that District human support service agencies administer. Figure 3 represents all the Federal money spent in FY18 by agencies clustered by their similar functions. Because of differing fiscal years and funding lifecycles, the District’s governmental activities by agency cluster and source of funding is specified through money spent in its public financial reports. Funding from the Federal Government for human support services constituted over 55 percent of the total District budgetary expenses in this category.[6] Every other category received and used an insignificant amount of mandatory funding for Federally mandated programs in comparison, following a recent pattern of historically low levels of Federal funds received outside of major health programs.[7]

Figure 3: Federal Revenues Spent by Agency Cluster in FY18

Note: Because of differing fiscal years and funding lifecycles, the District’s governmental activities by agency cluster and source of funding is specified through money spent in its public financial reports.

Private or Non-Profit Sector Revenue

Finally, grants and donations from private individuals, foundations, or organizations to the District Government are used to supplement District funding for mutually accepted policy goals. Like Federal grants, private grants and donations usually come with stipulations that are narrow in scope and go directly to the implementing agency such as the Public Schools. Agencies in the education cluster collectively received almost 39 percent of the total amount of private funding in FY18.[8] Figure 4 shows a breakdown of how much each agency cluster spent from private sources in FY18. Again, because of differing fiscal years and funding lifecycles, the District’s governmental activities by agency cluster and source of funding is specified through money spent in its public financial reports. The total money received from private sources in FY18 represented only 0.1 percent of the District’s total revenue received in that fiscal year.

Figure 4: Private Grants and Donations Spent by Agency Cluster in FY18

Note: Because of differing fiscal years and funding lifecycles, the District’s governmental activities by agency cluster and source of funding is specified through money spent in its public financial reports.

In future posts related to this series, we will discuss income taxes, property taxes, sales and excise taxes, the gross receipts tax and other taxes. This series, and the report behind it, focus on taxes levied in the District so Federal, private, and non-tax sources of revenue will no longer be discussed after this post. If you would like to see more of this kind of tax analysis, please visit the OCFO’s website at for the full Tax Facts Visual Guide report.

[1] D.C. Official Code § 47-401.

[2] Taylor, Yesim. “Land Value Tax: Can it Work in the District?” DC Policy Center, 21 Oct. 2019,

[3] District of Columbia’s Government. (2019). FY 2020 Approved Budget and Financial Plan. Washington, DC: Office of the Chief Financial Officer.

[4] U.S. Bureau of Economic Analysis, Total Gross Domestic Product by Industry for District of Columbia, retrieved from FRED, Federal Reserve Bank of St. Louis;, February 13, 2020.

[5] District of Columbia Comprehensive Annual Financial Report, Exhibit 2-d pg. 51, FY 2018

[6] DC CAFR, Exhibit D-2 Actuals, pg. 172-177, FY 2018

[7] The Center on Budget and Policy Priorities. “Federal Aid to State and Local Governments.” 19 April, 2018,

[8] DC CAFR, Exhibit D-2 Actuals, pg. 172-177, FY 2018

The Impact of Convention Center Conference Attendees on the City’s Economy

Business travelers are an increasingly important component of the hospitality sector and the District’s economy. In 2017, the District of Columbia attracted 22.8 million visitors to the city, approximately 41 percent of whom travelled here primarily for business and 59 percent primarily for leisure. This study analyzed the economic and fiscal impacts of conference attendees of the Walter E. Washington Convention Center (WEWCC). Convention center conference attendees are a small but important subset of business travelers to the District of Columbia because of the essential role they play in the of the 2.3 million-square-foot publicly financed WEWCC and the adjoining 14-story 1,175 room convention center hotel.


This study used three types of data.  First, the study started with monthly hotel sales tax data for each hotel in the city for the years 2005 through 2016. Second, the study matched the monthly hotel tax data to Destination DC conference booking data (such as dates, number of conference days, number of registered attendees, number of requested hotel rooms) for large conferences for years 2005 to 2016. Large conferences are those citywide conferences held at the convention center with 2,500 or more hotel rooms booked in the city on peak nights. And third, the study used citywide average daily hotel room rates (ADRs) and occupancy rates from Smith Travel Research (STR) for years 2005 to 2018.

Upon review of the conference data for all years, medical conferences are the only subset of conferences that continues to be substantially large in all years in terms of reserved hotels room nights. They alone account for almost half of all city hotel room nights attributable to convention center attendees. Accordingly, all convention center conferences for all years were categorized into two broad categories: medical conferences and all other conferences.

Medical conferences are a boon to the District

Compared to years 2005 to 2013, the average yearly conference attendance at the convention center for years 2014 to 2016 was down 6.1 percent even as hotel spending rose by 24 percent. (Estimates for hotel spending by conference attendees was possible using monthly citywide hotel sales tax data, which was only available for the years 2005 through 2016.) The main reason for this result is the growing role of medical conferences at the convention center. Whereas medical conferences attendees once accounted for 43.1 percent of all annual conference attendees at the convention center, in more recent years they accounted for 50.9 percent of all attendees and were responsible for 68.8 percent of hotel spending by all conference attendees (Figure 1). The study also estimates that, on average, medical conference attendees spent at least 35 percent more on hotel rooms than other conference attendees. In sum, the higher share of medical conference attendees, with their higher rate of spending, more than offset the spending impact of lower overall level of conference attendees to the District.

Source: Author’s calculations derived from WEWCC Conference, DC Office of Tax and Revenue and STR data.

Looking Ahead

Figure 2 shows convention center conference attendance for time period: 2005 to 2019. The figure also shows the estimated attendance based on confirmed bookings for years 2020-2022.

Source: Destination DC WEWCC Conference data.

The study finds the average attendance for years 2014 to 2016 for all conferences was 319,020. For years 2017 to 2019, the annual average attendance for all conferences was 53,725 (16.8 percent) higher. But, the annual average attendance for all conferences for years 2020 to 2022 (as of June 2019), is 15,059 (4.7 percent) lower than in years 2014 to 2016.  The number of confirmed conference attendees for 2021 and 2022 may be a bit concerning, but conference bookings are likely to only increase given that there is one to two years of additional time to secure additional conference bookings.  

For years 2005 to 2016, there was an average of 7.0 medical conferences and 14.8 all other conferences took place per year (Figure 3). However, for years 2017 to 2022, confirmed bookings indicate that there will be an average of 7.2 medical conferences and 16.7 all other conferences per year. Also, in years 2005 to 2011 the average conference length (number of conference days per conference) was 3.4 days for all conferences and 3.6 days for medical conferences. However, for years 2012 to 2016 the average conference length was 3.8 days for all conferences and 4.3 days for medical conferences. The recent trend of more total conference attendees, more medical conferences attendees, and longer conferences at the WEWCC is expected to increase the economic impact of future conferences to the city.


Convention center conference attendees are a small but important subset of business travelers to the District of Columbia because of their essential role in the publicly financed the District’s convention center and the adjoining convention center hotel.  In 2017, over 455,000 conference attendees (excluding inauguration attendees) visited the convention center, and they spent an estimated $116 million in hotel spending which in turn generated an estimated $16.8 million in hotel tax revenue (7.8 percent of total citywide hotel tax revenue). Given the national and local trends for business travelers and the hospitality industry, the relatively new convention center hotel, and the existing convention center conference bookings for years 2020 to 2022, it is possible that city hotel spending (and other subsequent spending in the city) by all conference attendees in years 2020 to 2022 may approach or even exceed that of 2017.

DC’s population reached 705,749 in 2019, almost 25% more than in 2005 when it started to grow

Increase slowed last year, however, with net in-migration averaging only about one person per day

The US Bureau of the Census estimates DC’s population on July 1, 2019 was 705,749, an increase of 4,202 (0.6%) from the revised estimate for 2018. 2019 was the 14th straight year of population growth. From 2005 to 2019 the city grew by 138,613, a remarkable gain of 24.4%. Also notable is that growth slowed in 2019, primarily due to decreases in net migration.

  • In the 14 years since 2015 the average annual gain in DC’s population was 11,241. The 4,202 gain in 2019 was 37.4 % of that average. 2019 was the slowest growth in 12 years and the smallest percentage gain in all the years since 2005.

graph 1

  • Natural increase accounted for 90.8% of the increase in population from 2018 to 2019, a percentage much higher than the annual average from 2010 to 2019, which was 40.2%. (Natural increase is births minus deaths.)

Net migration accounted for under 10% of last year’s increase as only 401 more persons moved to DC than moved away. Negative domestic migration (-2,203) was just slightly offset by positive international migration (2,604). The 401 net gain is an average of just over 1 person per day throughout 2019. By contrast, from 2010 through 2019 the daily average increase was 18 per day.

Revisions to prior year estimates. In preparing the July 1, 2019 population estimate, the Census Bureau also revised prior year estimates back to 2010. The most significant revisions were cuts to amounts in 2016, 2017, and 2018. For example, the new estimate for 2018 is 701,547, a reduction of 908 from the earlier estimate of 702,455.

Components of population change last year compared to the 9 1/4 years since the April 1, 2010 Census

The 4,202 increase in DC’s population from 2018 to 2019 was just 37.4% of the average annual increase since the April 2010 decennial census count. As already mentioned, the biggest change was in net migration, but other points are worthy of note as well.

  • In 2019 the number of births, 9,433, was 31 more than the annual average since 2010. Deaths, however, exceeded the decade average by 734, which explains why the natural increase in 2019 was below the average annual change since the last Census.
  • The fall in net domestic migration is quite striking. The average annual gain in the years since the Census is almost 3,000 per year. In 2019 there was a decline of 2,203.
  • Net international migration continues to contribute positively to DC population growth, but this has slowed as well. The amount in 2019 was just 71.2% of the annual average since the 2010 Census.

It should be noted that these Census estimates are summary statistics and leave out many important details, such as the number of households, the total number of people moving in and moving out, and the different characteristics of these persons. (how many are children, income, etc.).

DC and the US. For most of the years in which DC population has been growing, that growth has been at a rate faster than the nation-as-a-whole. In 2019 as its growth has slowed, DC’s 0.6% rate of increase got close to the US one (0.5%).

The share of the nation’s population in DC has been growing since 2007 when it was 0.191%. By 2019 it had climbed to 0.215%.

Comparison with the 50 states. In 2019 DC’s population was greater than that of Wyoming and Vermont. (The next closest states to DC are Alaska (731,545) and North Dakota (762,062).) Also from 2018 to 2019:

  • DC’s population increase exceeded that in 16 states (10 of which lost population). In percentage terms, DC’s 0.6% growth was faster than in 33 states.
  • DC’s natural increase was greater than in 11 states (4 of which were negative). DC had more births than 2 states (Vermont and Wyoming) and fewer deaths than two (Alaska and Wyoming). The natural increase in DC, however, was greater than in 11 states because in most states the number of births and deaths are closer in number than in DC.
  • DC’s net domestic migration, although negative, was less negative than in 20 states.
  • DC’s net international migration was greater than in 16 states.

About the data: The information reported here is from the tables released in December 2019 by the US Bureau of the Census in connection with population estimates for the 50 states and the District of Columbia as of July 1, 2019. The tables include (1) total population; (2) population as of April 1, 2010 in the decennial census and as of July 1 of each year from 2010 through 2019; (3) components of population change from July 1, 2018 to July 1,2019, and (4) components of population change from April 1, 2010, to July 1, 2019. The components of change are natural increase (with births and deaths shown separately) and net migration (with international and domestic migration shown separately. The data include revisions to the years 2010 through 2018.

An earlier version appeared in the December 2019 District of Columbia Economic and Revenue Trends report issued by the DC Office of the Chief Financial Officer.

Appendix table

Additional Neighborhood Effects of the City’s Housing Production Trust Fund Policy

The Low-Income Housing Tax Credit (LIHTC) is the largest federal program for the production and rehabilitation of affordable housing, and it supported 20,441 rental housing units in the District of Columbia in 2018.[1] In a recently published paper (see here and here), two Stanford University economists found that new affordable housing developments throughout the nation, funded by the LIHTC, tended to increase nearby home prices and incomes in their immediate areas.

Our current study assesses some of the local effects of affordable housing units on respective neighborhoods using District of Columbia income and property tax data. Specifically, the affordable housing units under analysis are financed in varying degrees by the District of Columbia’s Housing Production Trust Fund (HPTF). The HPTF is the city’s largest affordable housing policy tool that provides financing to help build and preserve affordable residential projects, and it financed over 9,000 affordable housing units in the city since 2001. Like the national study, this study also finds new affordable housing developments tend to increase neighborhood home prices and incomes. The study also finds that tax filers residing in HPTF housing units are more likely to claim the federal Child Tax Credit and claim higher amounts of the credit compared to comparable tenants in similar but non-HPTF rental units.

HPTF Buildings

We identified 740 HPTF-funded residential rental buildings across the city (Figure 1). And while there are HPTF-funded buildings in every ward, the vast majority of HPTF buildings are in the eastern half of the city. Notably, however, Ward 3 had only one HPTF building in 2019.

Figure 1. Location of HPTF Buildings by Ward

When examining the locations of HPTF buildings at the census tract level, we find there are HPTF buildings in 77 (43 percent) of the city’s 179 census tracts (see Figure 2).

Figure 2. Census Tracts with HPTF Buildings.

Neighborhood Effects

In order to identify the effects of HPTF-funded housing units on neighborhood home values and wage income, we identified individual income tax filers that lived in HPTF buildings in 2016. We aggregated all income tax information at the census tract level. We used the city’s property tax data to obtain each census tract’s median property value for single-family homes. In the study, the median property value in 2016 for all single-family homes and condos in the census tracts with HPTF buildings was $403,445. And, the median annual wage income in 2016 for the same census tracts was $30,529. 

Figure 3. Median Values for Census Tracts with HPTF Units in 2016

Source: Author’s Calculation derived from DC Office of Tax and Revenue data.

A statistical analysis at the census tract level identified the marginal effect of additional HPTF rental units and HPTF residents on different neighborhood outcomes for 2016. The model results indicate that an additional HPTF residential rental unit in a census tract is correlated with an average increase of $425.64 (0.11 percent) in the median home value, and an additional tax filer living in HPTF units is correlated with a $537.27 (0.13 percent) increase in the median home value (Figure 4). We also find an additional HPTF residential rental unit is correlated with an average increase of $7.71 (0.03 percent) in a census tract’s annual median wage income.

Figure 4. Average Marginal Effects on Median Home Values in Census Tracts

                                  Source: Calculations derived from regression analysis.

Individual Effects

The study also attempted to identify whether there was a statistical difference in the value of various federal and District credits, deductions, and exemptions for tax filers that resided in HPTF buildings and those in similar but non-HPTF rental units. The only statistically significant difference identified involved the federal Child Care Tax Credit. This study finds tax filers that reside in HPTF buildings are 2.2 percent more likely to claim the Child Care Tax Credit than income tax filers that live in similar but unsubsidized rental buildings after controlling for the number of children per tax filer, age, filing status and location of these tax filers. We also found that these tax filers receive a Child Care Tax credit that is on average $20 dollars higher (44 percent more) than those in the comparison group. (These credit amounts do not include the automatic District of Columbia Child Care Tax Credit that equals 32 percent of the federal credit for respective tax filers.) Though the actual credit amounts in this study are small on a per tax filer basis, the main takeaway is that these tax filers tend to spend more on child care services. Other research suggests that increased access to more or better-quality childcare can enhance children’s educational and social achievements and cause parents to miss less days from work in the short term. And in the longer term, it means longer-term financial well-being and earnings trajectory for both parents and children. This finding suggests that a possible side benefit of the city’s primary affordable housing program is improved prospects for children living in households benefiting from this program.


This study finds the benefits of the city’s HPTF program extend beyond merely providing affordable housing to low-and moderate-income households.  In terms of neighborhood effects, we find that an additional HPTF residential rental unit in a neighborhood is correlated with a 0.11 percent ($425.64) increase in the median home value, and an additional HPTF tax filer is correlated with a 0.13 percent ($537.27) increase in the median home value. We also find an additional HPTF residential rental unit is correlated with an average increase of $7.71 (0.03 percent) in a neighborhood’s annual median income.

HPTF buildings tend to be in neighborhoods with relatively higher poverty rates and lower employment rates. And since new affordable housing tenants are working adults that meet specified income requirements, it might be that new or newly renovated HPTF buildings help to increase the share of working adults (tenants) in respective neighborhoods, which in turn might mitigate some poverty-related characteristics of the neighborhood. Also, the new or newly renovated HPTF buildings represent an upgrading/modernizing of a neighborhood’s housing stock in that HPTF projects replace or help outnumber much older or even blighted residential buildings in respective neighborhoods. To some, this may be considered neighborhood revitalization, which in turn attracts higher-income residents to the broader low-income neighborhood.  In essence, the current drive of using the HPTF to help build and renovate affordable housing developments in low-income areas appears to be running parallel to the gentrification trends also taking place in many of the same neighborhoods. This too may be one explanation for the positive effects on neighborhood income.

This study also finds tax filers that reside in HPTF buildings have a higher probability of taking the federal Child Care Tax Credit, and that these tax filers receive a federal Child Care Tax credit that is 44 percent more than those in the control group.  This finding suggests that these tax filers could be using their savings from lower housing expenditures to purchase more or better-quality child care for their school age children. Or, the finding might indicate that tax filers who tend to spend more on child care may be more likely to choose HPTF housing (i.e. maximizing public benefits). Nevertheless, this study has identified several important positive side benefits (externalities) of the District’s largest affordable housing program.