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.

Some Effects of LEED Buildings in the District of Columbia

Since the passage of the DC Green Building Act of 2006 (DCGBA), the District of Columbia became the first major city in the United States to require all commercial and institutional buildings and projects with at least 50,000 square feet of gross floor area to meet or exceed LEED standards. Leadership in Energy and Environmental Design (LEED) standards are the most widely used green building rating system in the world and are designed to help buildings achieve high performance in key areas of environmental sustainability, human health, and energy efficiency.

The city has consistently led the nation in the number of LEED certifications, according to the U. S. Green Building Council (USGBC). This consistency over the years culminated in the city becoming the world’s first LEED Platinum city in 2017. In 2018, the city had 145 certified building projects with 37.1 million LEED-certified gross square feet. This amount of certified gross square footage represents 61.7 square feet of LEED-certified space per resident, the highest ratio among all states and major cities in the country (see here).

The DCGBA is one of the reasons commercial developers incorporate a substantial number of LEED standards into practically all new medium and large buildings and projects in the city.  With the residential development sector excluded from this local regulation, it appears that the law was intended to not subject the residential development sector to additional mandates and potentially higher construction costs (see here) possibly because of the current affordable housing crisis in the city. Oddly enough, there is still a modestly growing number of entirely new LEED residential buildings being built in the city. This analysis examines the effect of LEED-certification for new large multifamily buildings (which are wholly exempt from the DCGBA) on the rental rates of these buildings.

Class-A LEED Multifamily Buildings

Of the city’s 27 LEED-certified large multifamily buildings that were built after 2000, twenty-two were built after 2013 (Figure 1). In total, the city’s Class-A LEED-certified residential buildings as of 2018 accounted for 7.5 of the total 24.7 million square feet (30 percent) of all large multifamily buildings built after 2000. This class of buildings delivered an average of 1,184 residential units annually to the market since 2014 (Figure 2).

Multifamily Building Rents

To better examine LEED-certified large multifamily buildings, we select 22 (of the 27 total available) LEED-certified Class-A large multifamily buildings that delivered prior to 2019 (which are listed in Table 2) and compare them to 21 similar non-LEED large multifamily buildings. This comparison group of buildings are similar in age, location, building size, vacancy rates, number of floors, proximity to grocery stores, mix of unit sizes, and amenities.  Figure 3 shows the observed average effective rent per square foot (psf) for LEED buildings in 2018 was 15.2 percent higher than the average effective rent for non-LEED buildings. This suggests an average effective rent premium of $0.45 psf in 2018.

Projects pursuing LEED certification must meet requirements in several categories including location & transportation, sustainable sites, water efficiency, energy & atmosphere, materials & resources, and indoor environmental quality. The four LEED rating levels are Certified, Silver, Gold and Platinum. We further refine this statistical analysis by subdividing this study’s LEED buildings into two subgroups: top-tier LEED and bottom-tier LEED. Top-tier LEEDs are comprised of the highest categories of Platinum and Gold, and bottom-tier LEEDS are comprised of the lower designations of Silver and Certified.

Figure 4 shows the observed average effective rent psf for bottom-tier LEED buildings in 2018 was 13.8 percent ($0.41) higher than the average effective rent for non-LEED buildings, and the average effective rent for top-tier LEED buildings was 17.8 percent ($0.53) higher than that of non-LEED buildings.

Are Tenants and Residential Units in LEED Buildings Different?

Figure 3 shows the difference in the observed average effective rents for LEED and non-LEED Class-A large multifamily buildings in the city in 2018. To test whether there is truly a meaningful or significant difference in the averages of the two groups, as opposed to the difference being solely a product random selection, we conduct a t-test on the effective rents for the two subpopulations of buildings, as well as several other key variables.

We find there is a statistically significant difference in the effective rents, unit size, tenant income and tenant age of the two groups. Table 1 shows that residential units in LEED buildings are on average 68 square feet (8.0 percent) smaller than residential units in non-LEED buildings. Tenants in LEED-certified buildings have annual income that was on average $10,533 (11.7 percent) higher and tended to be marginally older than tenants in non-LEED buildings.  However, we did not find a statistically significant difference in the vacancy rates. CoStar is the data source for the 2018 rent, unit size and vacancy data, and the 2015 District of Columbia administrative individual income tax data is the data source for tax filer income and age for tenants of the 43 buildings under investigation.

The Effect of LEED-Certification on Rents

Even though we found that the observed differences in rents are statistically significant and not merely a chance event, it is unlikely that these differences are wholly attributable to LEED certification.

As a result of an additional statistical analysis of the effect of LEED certification on average effective rents of our 22 LEED and 21 non-LEED buildings in the city, we found that LEED certification does indeed have a positive effect on rents. Controlling for other factors, bottom-tier LEED buildings were found to have 9.7 percent higher rents than non-LEED buildings, and top-tier LEED buildings were found to have 11.4 percent higher rents than non-LEED buildings.  Applying these coefficients to the average 2018 effective rent of $2.97 for non-LEED buildings (see Figure 5), we find that the average rent premium psf was $0.29 for bottom-tier LEED buildings and $0.34 for top-tier LEED buildings instead of $0.41 and $0.53, respectively, as shown in Figure 4 before we controlled for the effect of these other variables.


The real estate sector plays a major role in the city’s economy, and it appears the sector has embraced the construction of LEED-certified buildings even prior to the enactment of the DCGBA. This is demonstrated by CoStar research that reports 33 Class-A office LEED-certified buildings delivered before 2011 when the full impact of the DCGBA went into effect.

Even though USGBC certified LEED buildings may, in some cases, be costlier to build at the outset, it is often posited that customer demand and long-term savings for end users resulting from a high degree of building efficiencies make LEED projects a good investment for developers. This notion seems to be supported by the findings that the city’s LEED Class-A multifamily buildings tend to command higher effective rents and attract higher income tenants while performing no differently from non-LEED comparables (i.e. vacancy rates that are low and not meaningfully different from other multifamily buildings).

The USGBC recognizes the District of Columbia as the nation’s leader with its 145 certified building projects with 37.1 million LEED certified gross square feet in 2018. Given that Nationals Park is the first LEED-certified Major League Baseball Stadium in the country and the Museum of African American History being the Smithsonian’s first project to receive LEED Gold certification (see here), it appears that the city’s residents, workers and visitors value new buildings and facilities that are simultaneously innovative, attractive, inspiring and environmentally-friendly. As the city grows its LEED-certified footprint, it is likely that the city will remain a LEED Platinum city in the years to come.

DC’s exposure to negative impacts from a US recession is probably growing

DC’s private sector is now a larger share of the economy, and the City cannot count on increased federal spending to offset negative impacts

The U.S. economy is about to enter the longest period of economic expansion in its history as it passes the 10-year mark in June. Although a possible recession is not evident at this time, at some point a recession it is likely. When that happens, the negative impact on DC’s economy would depend on the nature and severity of the recession. However, as explained below, DC’s economy is not insulated from recessions, and exposure to the negative impacts from a recession is probably growing.

  • The negative impacts on jobs and wages in DC’s private sector in the past two recessions were similar to, although somewhat less severe, than the impact on the private sector nationally when manufacturing and mining are excluded from the calculation. (See graph of wages below.) There is no obvious reason to expect this to relationship to be significantly different in a future recession.
  • Federal spending gave a disproportionate boost to DC’s economy in the past two recessions, mitigating some of the negative factors confronting DC’s private sector. Current federal fiscal policy makes it less likely that such increases in federal spending can be counted on in the future.
  • The share of DC’s economy represented by the private sector has been rising, increasing the economy’s exposure to a downturn.
  • Jobs and incomes of DC residents were harder hit than those associated with jobs located in DC in the last two recessions. Being hit harder may not necessarily be true in another recession because DC’s economy has become stronger, but a substantial negative impact on residents would still be expected.
  • The negative impacts of the 2007 recession in DC’s suburbs were generally similar to those that occurred in the District of Columbia. Private sector jobs fell a little more sharply in the suburbs,  wages and salaries fell more sharply in DC, and the percentage drop in resident employment was about the same in both locations.

chart 1

Recession and DC’s private sector. In the 2001 and 2007 recessions, the manufacturing and mining sectors of the U.S. economy, which comprise around 15% of the U.S private sector, were hit very hard. In the 2007 recession, for example, wages and salaries in the manufacturing sector declined 13.9% from 2007.2 to 2009.3. The very large remaining part of the U.S. private sector was also adversely impacted by the recession, but not nearly as much, with wages and salaries falling 2.9% over the same nine-quarter period. The recession’s impact on DC’s economy was nothing like that on places with a lot of manufacturing and mining.  However, neither was DC insulated from adverse effects. This is evident by comparing what happened in DC with the experience of the part of the U.S. private sector that does not include manufacturing and mining.

The graphs of changes in both jobs and wages in DC’s private sector during the 2001 and 2007 downturn show a similar pattern to the US private sector with manufacturing and mining left out. The extent of the downturn in DC’s private sector is somewhat less, however. For example, a one year decline of 3.2% in private sector jobs in DC in the 2009.3 quarter compared to 4.8% in the private sector nationally (with manufacturing and mining excluded). Similarly, the one year drop in wages in DC’s private sector in the 2009.1 quarter was 4.1% compared to 6.0% in the US.

In a future recession, it would be reasonable to assume that the exposure of DC’s private sector would again at least be similar to that in the US as a whole (excluding manufacturing and mining sectors). However, one reason the private sector impact was lower in DC than for the similar sectors in the US in the last two recessions was the relatively large boost in federal spending that benefitted DC’s economy as the recession unfolded.

Chart 2Table 1.PNG

Federal spending. In the 2001 and 2007 recessions, federal spending increased, providing a counter-cyclical boost to the economy. Because the federal government accounts for such a relatively large portion of DC’s economy, this spending has provided a much more substantial boost to DC’s economy than was true nationally. (Federal jobs account for 25% of the jobs in DC compared to 2% nationally, and federal wages and salaries account for 31% of the total earned in DC compared to 3% nationally. Looked at another way, DC accounts for about 7% of all federal jobs and about 9% of all federal wages and salaries.

The proportionately greater boost that DC received from federal spending is evident in the change in wages and salaries that occurred in DC and the US from 2008.1 to 2009.1, a year right in the middle of the Great Recession. Federal wages and salaries in DC increased by $1.02 billion in that year, equivalent to 75% of the loss in private sector wages. For the US, the increase in federal wages was equivalent to only 2.3% of the private sector decline. The increase in federal wages and salaries in DC was 15.8% of the US total.

In a new recession it would appear that DC cannot count on receiving such a relatively large boost from federal spending. Under current federal fiscal policies, debt is increasing even as the economy expands. If controlling the level of federal debt is a policy concern at the time another recession occurs, counter-cyclical spending is likely to be much more moderate, if it happens at all, and the proportion spent in DC may be less.

table 2

chart 3.PNG

Private sector share of the economy. In the years since the 2007 recession the private sector share of DC employment and wages has been rising. In 2009.2, the recession’s last quarter, DC’s private sector accounted for 66.1% of all jobs in DC and 59.9% of all wages in the economy. By the 2018.4 quarter, these shares had risen to 70.2% and 64.6%, respectively. With this rise in the private sector’s share of the economy, it is reasonable to expect the potential for negative impacts on the DC economy from a US recession also to rise.

chart 4

Recession impact on DC residents. In the 2001 and 2007 recessions the jobs and earnings of DC residents (some of whom worked outside of DC) were more adversely affected than were the jobs and earnings related to all the jobs located in DC. In the 2001 recession, resident jobs and wages fell more quickly in the first quarter of 2001 when the recession began. The consequences for DC residents then lingered, which is likely related more to the shock of 9/11 than to the recession itself (9/11 occurred before the recession ended).

In the 2007 recession, neither the jobs located in DC or those for DC residents fell right away when the recession began. By the second quarter of 2009, however, resident jobs were 2.3% lower than a year earlier compared to 0.9% lower for all jobs located in DC. One year into the recession, the earnings of DC residents went from growing at a very fast 7.6% rate in the 2017.4 quarter to –1.0% in the 2018.4 quarter. Earnings of all those working in DC also fell, but not quite as much—from a 6.1% growth rate in 2017.4 to 2.5% in 2018.4.

The jobs and earnings of DC residents surely would be adversely affected by a new recession, but there have been changes in the economy that might mean the impact would be more like —or even less than— that affecting jobs and earnings for all working in DC. For example, DC’s population began to grow in 2005 and it is now 128,051 (22.3%) higher than it was in 2007 when the recession began. From calendar year 2009 to calendar year 2018, DC resident jobs grew faster than jobs located in DC (25.5% versus 12.9%), and the growth in amounts earned by DC residents also well outpaced the amounts earned in DC (64.5% versus 42.4%). Also, DC resident jobs and earnings recovered faster from the 2007 recession, and the growth of resident jobs and earnings also generally remained above jobs and earnings for all persons working in DC for the period of the sequester (FY 2013) and subsequent years.

chart 5

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DC and the Washington area suburbs. The negative impacts of the recessions were generally similar for DC and for the Washington suburban area, but there were a few differences. In the 2001 recession (which included the 9/11 attack), private sector jobs located in DC fell more sharply than those located in the suburbs, but suburban private sector wages fell more than amounts earned in DC. In the 2007 recession, the differences were reversed: the suburbs experienced more private sector job decline and less decline in private sector wages and salaries. In the 2007 recession, the percentage declines in resident employment in DC and the suburbs were about the same.

The appendix charts show changes in private sector jobs, private sector wages and salaries, and resident employment for DC, the Washington area suburbs, and the US.


chart 7

chart 8


Chart 11


About the data: The wage and salary employment data for DC, the Washington area suburbs, and the US is from the US Bureau of Labor Statistics (BLS), as is the data on resident employment in all locations. Most of the data shown here are based on quarterly data calculated from data that is not seasonally adjusted. Washington suburban area data is calculated by subtracting the amounts for the District of Columbia from metropolitan area totals.

Data on earnings and wages and salaries in DC and the US are from the quarterly Personal Income accounts compiled for states and the US by the US Bureau of Economic Analysis.

Data comparing jobs and wages and salaries in DC, the Washington area suburbs, and the US were accessed through Moody’s Analytics. The data are all seasonally adjusted.

All of the data are subject to further revision next year.

US business cycle expansions and contractions are officially designated by the National Bureau of Economic Research (NBER). According to NBER, the 2001 recession was a brief one, extending only from March 2001 (the first quarter) to November 2001 (the fourth quarter). Particularly for DC, the occurrence of the 9/11 attacks in the midst of the recession makes it more complicated to discern only the recession’s impact on DC.

NBER defines the Great Recession of 2007 as running from December 2007 (the fourth quarter) to June 2009 (the second quarter).

An earlier version of this blog appeared in the April 2019 District of Columbia Economic and Revenue Trends issued by the DC Office of Revenue Analysis of the DC Office of the Chief Financial Officer.


Charts of (1) private sector jobs,  (2) private sector wages and salaries, and (3) resident employment for DC, the Washington area suburbs, and the US.

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Chart 11


Chart 12

The curious connection between DC population growth and new Class A residential buildings

The new buildings are clearly related to DC’s population growth, yet population growth slowed as building increased

According to the US Census Bureau, DC’s population started growing in 2006, and in the 13 years from 2005 to 2018 DC added 135,319 people, a 23.9% increase. Over that same time, according to CoStar, there was a net increase of 303 multi-family residential buildings containing 40,388 multifamily housing units, a 27.6% increase.


table 1


Of the increase in multi-family housing, 74.3% of the buildings and 84.3% of the units occurred in what CoStar classifies as Class A buildings. These are newer, well-located apartments and condominiums with modern amenities. About 80% of the Class A units are apartments (see the appendix for more detail). Over the 13 years from 2005 to 2018 there was a 446% increase in units in Class A buildings compared to a 4.6% net increase in all other units.


graph 1


That there has been a significant increase in multi-family buildings and units is not surprising to anyone familiar with all the cranes that have dotted the city for more than a decade. What may be surprising, however, is the extent to which a relatively large proportion of the residential construction, especially that of Class A units, lagged population growth and occurred as population growth was actually slowing. The proportion of the entire 2005 to 2018 population growth that had occurred by 2013 was much greater than proportion of new housing units that had occurred by that date:

  • By 2013 DC had added 60.8% of the population growth that occurred from 2005 to 2018, but only 44.2% of the net increase in all multi-family housing units over the entire period—and only 39.0% of those in Class A structures.
  • From 2013 to 2018 DC added 38.4% of the population growth that occurred from 2005 to 2018, but a much greater share (55.8%) of the net increase in all housing units in multifamily structures occurred then. The share of Class A structures added after 2013 was 61%.
  • In 2013, the year of the largest annual gain in population, DC added 15,706 people and 2,078 net new multi-family housing units. In 2018 population growth was less than half (6,764) of what it was 5 years earlier while the net increase in housing units in multifamily buldings was more than twice as much ( 4,408).


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graph 2


From the CoStar data on housing it is not possible to explain all of the dynamics that link population changes in DC to housing market developments. Clearly until 2013 most of the increase in population did not find housing in new Class A buildings. As we approach 2018, however, a much higher percentage of the growth could be housed in such units.  In 2013 the ratio of population growth to net increase in multifamily units of all classes was 7.6.  In 2018 that ratio has fallen to l.5. This means that the entire net increase in population in 2018 could have been housed in new (mostly Class A) housing if the average household size was 1.5.


Going forward, an interesting question is how much of a limiting factor the availability of new Class A housing may to population growth in DC.  There are quite a number of factors at play and so it is difficult to draw a firm conclusion here.  For example, not all Class A units that are occupied (or rented) are necessarily occupied or rented by residents who would be counted by Census as part of DC’s population. This could involve persons whose primary residence is in another state, units owned for temporary housing of corporate personnel, or units owned for short term rentals. Also if persons now sharing units in DC move to newly constructed ones to live by themselves, occupied units would increase without any increase in population. The full story linking population and housing market changes must, of course, take account of all housing units in the city, not just those in Class A units in multi-family structures.


As noted above, most of the Class A buildings are apartments. More details on Class A residential buildings are contained in the appendix.

About the data: 

This is the second of two blogs dealing with  population dynamics in the District of Columbia in relation to the latest  US Census Bureau estimates of DC population.

The population information reported here is from the DC population tables released in December 2018 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, 2018.  The data include revisions to the years 2010 through 2017, and all information is subject to further revision next year.

Housing data is from CoStar, a real estate information firm that tracks all private sector apartment and condominium housing units in multifamily buildings with 5 or more units. Information for each year is for the second quarter, which corresponds closely to the July 1 date used by the Census Bureau for estimating annual population numbers. CoStar data is continuously updated and revised as more information becomes available.

A version of this blog appeared in the January/February District of Columbia Economic and Revenue Trends report, issued by the DC Office of Revenue Analysis.


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DC’s population topped 700,000 in 2018, but last year also saw the slowest annual increase in a decade

Natural increase, not net in-migration, has become the main source of DC’s population growth.

The US Bureau of the Census estimates DC’s population on July 1, 2018 was 702,455, an increase of 6,764 (1.0%) from the revised estimate for 2017. This is notable for several reasons:

  • 2018 is the 13th straight year of population growth. From 2005 to 2018 population grew by 135,319, a gain of 23.9%.
  • 2018 is also the slowest population growth in a decade. From 2008 to 2018 DC’s population grew an annual average of 12,222. Growth in 2018 was just 55.3% of the
  • decade’s average.
    graph 2


graph 1


  • 60.7% of the net increase in population from 2017 to 2018 was accounted for by natural increase of 4,104. (Natural increase is births minus deaths.) The rest of the net change was from international migration. For migration within the US, 936 more people left DC than moved here.


graph 3


  • DC’s population was larger than that of 2 states (Vermont and Wyoming), and last year’s growth was more than in 16 states (9 of which actually lost population).
  • In percentage terms, DC’s rate of growth was well above the US (0.6%) rate and above that for 36 states.

Additional details on recent growth, revisions to past years, population change since 2000, and comparison with the 50 states are included below.


Revision to the 2017 estimate. The July 1, 2018 DC population estimate of 702,455 is actually 8,463 higher than last year’s 2017 estimate. However, the increase over 2017 is now estimated to be 6,764 because revisions to population estimates in earlier years have resulted in a new estimate for 2017 that was 1,719 higher.   The current estimate for 2017, 695,691, is 1,719 less than the prior one (693,972).

As shown in the accompanying table and graph, the revision to prior years involved all the years from 2010 to 2017. The higher estimate for 2017 reflects the cumulative impact of increases and decreases since 2010. Although the level in 2017 was higher, growth in 2017 over 2016 was actually lower (by 520 persons).

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Components of population change last year compared to the 8 1/4 years since the April 1, 2010 Census. The 6,764 increase in DC’s population from 2017 to 2018 was just a little over half (55.4%) of the 12,205 annual average since the April 2010 decennial census count. Several features stand out:

  • Natural increase has become the major contributor to DC’s net population growth. This past year natural increase accounted for 60.6% of the increase; its average contribution since 2010 was 37.4%. Although births and deaths last year both exceeded their annual averages since the census, natural increase was less than the post-census average because deaths increased more than births.
  • Domestic migration is no longer a source of net growth for DC. Net domestic migration contributed 29.3% to growth since the census, but now more people are leaving DC for other parts of the US than are arriving from them.
  • International migration has become a more important source of growth for DC. It was the source of 32.0% of the net increase in DC’s population in the years since the census, and 53.1% of the growth last year.

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Comparison with the 50 states. In 2018 DC’s population was greater than that of Wyoming and Vermont. (The next closest states to DC are Alaska (737,438) and North Dakota (760,077)), Also, from 2017 to 2018:

  • DC’s population increase exceeded that in 16 states (9 of which lost population).
  • DC’s natural increase was greater than in 11 states (2 of which were negative).
  • DC’s net domestic migration was less negative than in 26 states.
  • DC’s net international migration was greater than in 18 states.


table 3

Details on comparisons with the states for 2018 and for changes from 2017 to 2018 are shown in the appendix.

About the data: This is the first of two blogs dealing with aspects of population growth in the District of Columbia.

The information reported here is from the tables released in December 2018 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, 2018. Those 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 2018; (3) components of population change from July 1, 2017, to July 1,2018; and (4) components of population change from April 1, 2010, to July 1, 2018. 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 2017.

A version of this blog appeared in the December 2018 District of Columbia Economic and Revenue Trends, issued by the D.C. Office of Revenue Analysis.


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The District of Columbia Reforms its DCEITC for Childless Workers

In 2015, the city replaced its local Earned Income Tax Credit (DCEITC) policy for childless workers as a fixed percentage of the Federal Earned Income Tax Credit (EITC) with a formula that appreciably increased both the local credit amounts and maximum eligible income. (See full policy brief here.) In this new formula, the city applies the federal maximum credit amount to a much larger income range ($6,600 to $18,111) and the federal phase-out credit rate to higher income levels ($18,111 to $24,040). As shown in Figure 1, the DCEITC encompasses all the city’s childless workers eligible for the Federal EITC plus a substantially larger number of workers who earned between $14,800 and $24,040 in annual wage income in 2015.


In 2014, 53,839 tax filers received the EITC and DCEITC. These were comprised of 41,391 filers with qualified children and 12,448 without qualified children (Figure 2).  With the expansion of the DCEITC for childless workers in 2015, there was a 26.8 percent increase in total DCEITC claimants with nearly all the increase being attributed to 12,490 new childless workers (2,983 who were also eligible for the Federal EITC and 9,507 who were still not eligible for the Federal EITC and not previously eligible for the DCEITC).


Structure of the Childless Worker EITC: DC v. Federal

A comparison of the structure of the expanded DC childless worker EITC with the federal credit focuses on the five income ranges. In Figure 3, the first income range (wage and salary income less than $6,600), labelled Income Range 1, is the phase-in range for both the federal and District credit, where both the federal and local credits increase by 7.65 cents for every additional dollar of earnings. The second income range (income between $6,600 and $8,250), Income Range 2, is the plateau of the EITC structure, where the credit is $503 for both the federal and local credits, regardless of earnings. Income Range 3 (income between $8,250, and $14,800) is the phaseout range for the federal credit, where the federal credit is reduced by 7.65 cents per dollar of earnings, while the District credit remains at $503 regardless of earnings.  For Income Range 4 (income between $14,800 and $18,111) each childless worker receives a DC credit of $503 regardless of earnings but receives no federal credit. And for Income Range 5 (income between $18,111 and $24,040), a DC childless worker receives a DC credit that decreases by 7.65 cents for every additional dollar of earnings up to an income level of $24,040. Beyond $24,040 in income, a resident is ineligible for the DCEITC childless worker credit.


The DCEITC Impact on Low-income Households

Table 1 shows how the DC childless worker credit varies across the identified income ranges and compares the DC childless worker credit to that of the federal’s for Tax Year 2015, the first year the new DC credit was in effect. In 2015, there were 2,983 new claimants who were eligible for both the Federal EITC and the DCEITC (Income Range 3), and 9,507 new DCEITC claimants who were not eligible for the Federal EITC (Income Ranges 4 and 5). Tabl1_After Fig3


In tax year 2015, the District expanded the eligibility for its local credit for childless workers by extending the credit to childless workers with income from $14,800 to $24,040; it also increased the local credit amounts. The maximum Federal EITC for a childless worker in 2014 was $496, and the maximum local credit amount a childless worker could receive was $198 (Figure 4). Under the 2014 policy, childless workers in a narrow range of income ($6,600 to $8,150), making up 12.8 percent of all DCEITC childless workers, claimed a combined maximum amount of $694. Workers that earned more than $14,550 that year were not eligible for either the federal nor local credit.


Under the new 2015 policy, 12,632 claimants (50.7 percent of all DCEITC childless workers) that earned between $6,600 to $18,111 in income received a DCEITC of a maximum amount of $503 (Figure 5). And further, claimants with income between the narrow range of $6,600 and $8,250 received a combined EITC and DCEITC of $1,006. Stated differently, childless workers with income less than $8,250 received a DCEITC that was a 100 percent match of their federal credit, and claimants with income between $8,250 and $14,800 received a DCEITC that was an average of 192.3 percent more than their Federal EITC.


The DCEITC in a Changing District

The expanded DCEITC for childless workers provides additional income support and security for the city’s lowest income earners, encourages labor market participation, and helps to facilitate poverty alleviation. But, it may also be incentivizing continued city residency for a growing number of the city’s lowest income earners through higher refundable tax credits that could be used to help counter the rapidly rising costs of living in the city.


Are growth of the labor force and resident jobs slowing in DC? Maybe—and maybe not.

Seasonally-adjusted and unadjusted data from BLS currently tell different stories

Each month the US Bureau of Labor Statistics (BLS) estimates labor market statistics for all states and the District of Columbia. Labor market statistics include the labor force, resident employment, unemployment, and the unemployment rate. The data is reported on both a seasonally adjusted and unadjusted basis. Seasonal adjustment takes account of recurrent events during a year such as holiday employment that can mask trends in the data.

Typically, comparing data from the same month of the prior year eliminates the need for seasonal adjustment. Accordingly, it would be expected that there should be little difference between seasonally adjusted and unadjusted estimates of the annual change in DC resident employment from September 2017 to September 2018. Currently, however, the two data sets give very different pictures of the change over this time, leaving unanswered the question as to whether DC’s resident employment is or is not slowing significantly.

  • The seasonally-unadjusted data say that from September 2017 to September 2018 resident employment (measured by the 3-month moving average) increased by 3,870 (1.0%). By contrast, the seasonally-adjusted data peg the increase at twice that (8,067, a 2.1% gain).
  • The unadjusted data show quite a sharp decline in the amount of year-over- year growth since May 2018, while the adjusted data show an increase.
  • The unadjusted data peg growth over the past year at about half the annual average increase over the past 5 years. Seasonally adjusted, the growth is very close to the average of the past 5 years.          

Details are shown in the tables and charts in the appendix. As indicated there, the story is similar for the seasonally adjusted and unadjusted estimates of DC’s labor force.

The current difference between the one year change in the seasonally adjusted and unadjusted resident employment and labor force data is an unusually clear example of the difficulty in spotting changes in the economy by closely monitoring data as it is released each month or each quarter. As with Personal Income, population, and other data produced by federal agencies, labor market data is revised as more information becomes available.

As the labor force data is revised the current differences in the story about changes over the past year told by the seasonally-adjusted and unadjusted data will be resolved. However, it will likely not be until March 2019 when major annual revisions typically occur that the matter will be cleared up.

It should be noted, however, that the seasonally-adjusted and unadjusted data both tell the same story about unemployment: the amount and rate of unemployment fell over the past year.











About the data. The labor market information is from the statistics released each month for the District of Columbia (along with all states) based on a population survey. The data include resident employment (persons over 16 years of age who say they are working on a full or part time basis); unemployment (persons over 16 years of age who are not working but say they are looking for work); labor force (the sum of resident employment and unemployment); and the unemployment rate (unemployment as a percentage of the labor force).

The data are reported on both a seasonally adjusted and not seasonally adjusted basis. For the month of September 2018 the data reflect the revisions which were part of the October 2018 release. The annual comprehensive revision to the data will occur in March 2019. All calculations here are based on 3-month moving averages (e.g., September 2018 is the average of July, August, and September as reported by BLS).

Seasonal adjustment is a statistical method for removing the seasonal component of a time series that exhibits a seasonal pattern. It is usually done when wanting to analyze the trend of a time series during a year independently of the seasonal components. It is common, for example,  to report seasonally-adjusted data for unemployment rates to reveal the underlying trends in labor markets.

An earlier version of this blog was included in the October 2018 District of Columbia Economic and Revenue Trends report issued by the Office of Revenue Analysis of the District of Columbia Office of the Chief Financial Officer.