The Federal government’s presence in the DC property market: Q&A on the impact of the Federal “Reduce the Footprint” policy on the commercial real estate market.

Co-author: Peter Johansson

In March of this year the Administration enacted the National Strategy for Real Property (National Strategy) and the Reduce the Footprint (RTF) policy. This directive expanded the scope of the prior Freeze the Footprint policy, which required all Federal agencies to freeze their real estate footprint. As stated in the press release, “With the establishment of the National Strategy and OMB’s new Reduce the Footprint (RTF) policy agencies will be required not only to continue to freeze but also reduce their real property footprint over the next several years. ” In addition to the Federal government, foreign governments have seemingly adopted a similar policy to reduce their footprint and capitalize on their real estate holdings as evidenced by the conversion of the former Italian embassy in DC to condominiums and other similar real estate dispositions by foreign governments.

In this post we conduct our own Q&A to analyze the potential impact of this policy on the commercial real estate market and to analyze the broader market of tax-exempt properties.


Q: To what extent has the District’s economy diversified away from the government sector and been able to absorb the space that resulted from the reduction in the government’s footprint?

A: In 2005 private sector employment accounted for 66 percent of all employment in DC. Today it accounts for 69 percent of total employment. Over this 10 year time frame, the private sector added 78,000 jobs while the Federal government added only 3,500 jobs. The sequestration had a large impact on Federal employment which experienced 3 consecutive years of declines beginning in 2011. More recently, Federal employment has stabilized. This is good news for the District’s economy but looking forward the private sector will still be the major engine of growth.

Q: What are some examples of former Federal properties that have been converted or occupied by private tenants?

A: Notable recent examples of this diversification include the conversion of the Old Post Office to a hotel and the planned redevelopment of the FBI headquarters building. Both of these properties are on prime real estate on Pennsylvania Avenue. In some cases, rather than dispositions and conversions, Federal agencies have relocated within the District, but the net effect has been a reduction in the Federal government share of the overall market’s footprint.

Q: What effect has this diversification had on the District’s real property tax base?

A: Concurrent with this shift in the employment base has been a decline in the share of market value of tax-exempt commercial properties as shown in the graph below. The market value of tax -exempt commercial properties, 49 percent in 2005, has declined to 47 percent in 2015.

Market Value ($ mil) and Share of DC Tax-Exempt and Non-Exempt Commercial Properties ,2005 vs. 2015 


Q: What has the impact on tax revenues been?

A: While a two percentage point increase in the share of taxable properties is small, this change represented almost $3.5 billion dollars in market value and accounts for $59 million dollars in annual tax revenue. This isn’t to say that by virtue of the Federal government reducing its footprint, revenues will necessarily increase. Someone has to occupy this space and while some Federal properties may be highly desirable and a target for conversions (like the Old Post Office),  other properties which lack amenities or floorplates suited for today’s office market, may languish on the market.

Q:The District still has a very high share of tax-exempt properties. What agencies, or who, accounts for this large share?

Share of DC Tax-Exempt Commercial Properties: by Sector, 2015 


A:  The Federal government still accounts for the lion’s share of tax-exempt properties with 59 percent of the overall market value. The other large tax-exempt sectors are not-for-profits, which include educational institutions, and make up 23 percent followed by the DC government, which accounts for 15 percent.   Foreign governments hold 3 percent and public authorities like WMATA account for 1 percent.

Q: Is the presence of such a large share of tax-exempt properties unique to the District?

A: Surprisingly no.  New York City for example has an even higher share.  Almost 53 percent of market value in New York City is tax-exempt, higher than DC’s 47 percent.  The sectors that account for this, however, are very different from those in DC.

Market Value ($ mil) and Share of NYC Tax-Exempt and Non-Exempt Commercial Properties, 2015 


Q: How can New York City have a higher share than the District which is the seat of the Federal government?

A:  Indeed the Federal government’s share of tax-exempt properties in New York City is far lower than in DC. However, public authorities, like the Port Authority of New York and New Jersey, which owns airports, land and properties including the World Trade Center site, have a much larger presence there than in DC. Even the land that was excavated from the World Trade Center site was used to create Battery Park City, owned by another public authority, the Battery Park City Authority.

Share of NYC Tax-Exempt Commercial Properties by Sector, 2015


Q: Do the District and the DC Area still have greater exposure to the Federal Government Reduce the Footprint policy, at least for office properties?

A: Yes the direct impact of this policy is likely to be greater in DC and the surrounding area than in other large office markets.  The indirect impact of this program and Federal budget cutbacks is broad based and felt throughout the nation. Transit agencies face huge funding needs for their capital programs. To the extent that the Feds are cutting back on funding, local authorities and government increasingly have to capitalize and sell their real estate holdings to finance these needs.

We will have to see the details regarding the implementation and timing of this program. The pace of implementation, the quality of space being put on the market and the strength of private sector office using employment will all be critical factors in determining the impact this program will have. In some cases this could be a real opportunity for the private sector to step in to reposition or redevelop government property.

What exactly is the data?

DC property tax data is from the DC Office of Tax and Revenue. New York City data was obtained from the NYC Department of Finance annual Property Tax Report.  NYC market values were calculated assuming a similar ratio of market value to tax for non-taxable and taxable values.

Bob Zuraski contributed to this post

Income mobility- what are the chances of moving up the income ladder? Evidence from DC taxpayer data

It has long been a tenet of American society that income disparity is more acceptable provided that there is a reasonable chance that someone who starts poor can make their way up the economic ladder to at least middle class status through education and work. This is the premise of the American dream and of a society based on the principle of meritocracy.  In this post we use DC taxpayer data to analyze income mobility, the extent to which an individual’s income changes over time. This data allows us to determine the probability of an individual moving up the rungs of the economic ladder.  The analysis also determines how far up one is likely to move up the economic ladder, starting at the bottom.

We focus initially on singles since income mobility is easier to define for a single individual than for married couples, where income is defined on an aggregate basis.  For a married couple, upward mobility could be the result of various outcomes- both spouses moving up the ladder simultaneously, one spouse moving up the ladder and the other remaining steady, or some other combination.

We examined mobility for singles by looking at where individuals stood on the economic ladder in 2002 and compared this to where they ended up in 2012. The percentage of filers who changed positions on the ladder measures the probability of moving on the ladder.

Here’s how to interpret the results shown in the matrix below:

Reading across starting from the top row, 39 percent of individuals who were in the bottom 20 percent of the income distribution in 2002 remained in the bottom twenty percent in 2012, 28 percent moved up one rung of the ladder to the second quintile (20th to 40th percentile), 15 percent to the third quintile and so on for the other quintiles. Similarly for the second row, 18 percent of individuals who were in the second quintile in 2002 fell down the ladder ending up in the lowest quintile in 2012, 35 percent remained in the same quintile and so on. Shaded boxes in yellow denote no change in income status, blue shading denotes upward mobility and red denotes downwards mobility.

Income Mobility: Chances of Moving on the Economic Ladder by Income Range, 2002-2012


Source: DC Income Tax Data 2002-2012, DISTRICTMEASURED.COM


  • Thirty nine percent of singles who started poor (in the lowest quintile) remained poor after a decade.
  • The median age for filers stuck at the bottom was 49 years. Given that these individuals are well into their career paths, the chances of their income prospects improving in the next ten years are likely to be small.
  • Twenty eight percent who started at the bottom moved up one rung of the ladder, and 33 percent from the lowest quintile made it to middle class status or higher (40th percentile and higher).
  • For those starting in the second quintile in 2002, the chance of moving up to middle class status one decade later was 47 percent.
  • The likelihood of remaining in the middle class in 2012 for those already in the middle class in 2002 was greater than 25 percent.

What can we conclude from the data? In a society where income mobility mitigates some of the worst effects of an unequal distribution of income, an individual through increased work experience and skill acquisition, would likely experience at least one movement up the ladder over a ten year period, or stay steady if she or he is at the top rung (See further discussion below).  While the data indicates that this is the case for most filers who started in the top three quintiles, for those on the lowest rungs of the ladder (the lowest two quintiles) the chances of moving up are only about 50/50.

Various factors have been cited to explain the scarce mobility of individuals at the bottom of the income distribution.  In a previous post we explored whether career paths contributed to this lack of mobility and found that increasingly the occupation one is employed in influences this outcome.  An employee in retail or education will have a hard time moving up the ladder. Other studies have focused on other factors such as increasing cost of college education, the decline in unionization rates and free trade to explain this lack of mobility.

What exactly is the data?

Data is from the 2002 and 2012 DC income tax returns for single Individuals excluding senior filers. 22,742 single filers were in the data for both years.  Income quintiles represent the following income ranges.

Income Quintile  Income $
Lowest 20th Less than $20,000
20th to 40th $20,000-$35,000
40th to 60th $35,000-$50,000
60th  to 80th $50,000-$85,000
80th  to 100th Greater than $85,000

Is a ten year time frame sufficient to consider income mobility?

A simple framework to analyze this is to consider how a person’s income would progress over a typical career path of approximately 40 years (say from ages 25-65) in a world where there was mobility across a wide range of career opportunities and access to education. This individual would begin her/his career at age 25 at or near the low-end of the income scale.  Through increased work experience she/he would move up a rung on the ladder and by their mid-thirties be at or near middle class, and after another decade, in their 40’s, expect to move up to achieve upper middle class status. In this career path each rung of the income ladder can be thought of as about 8 years (40 years /5 quintiles), so that after a decade an individual should move up at least 1.25 rungs.

Other considerations- Because the data is limited to DC filers, the analysis provides a more limited view of income mobility for the nation as a whole, as individuals can move to other states to seek better economic opportunities. While this is a limitation of the analysis, it is important to note that during the 2002-2012 timeframe the DC economy was one of the strongest performing in the nation. The probability that an individual could move up the ladder by moving to a different state was not likely to be high during this timeframe.

Contributors to this post:

Betty Alleyne, Bob Zuraski


The return of mega deals in DC’s and the nation’s commercial real estate market.

Not only are prices for residential properties in the District reaching new heights but sales of commercial properties (office, hotel and apartments) are also booming in the District. This is part of a larger national trend that reflects strong investor appetite for U.S. commercial properties.

Fueling demand for commercial properties is the healthy state of the U.S. economy and the consistent pace of employment gains over the past years.  These employment gains have helped to drive down vacancy rates and put upward pressure on rents.  With vacancy rates approaching equilibrium levels in many markets, rent spikes are likely on the horizon, music to the ears of investors.

In addition to strong internal fundamentals, the bright outlook for the U.S. economy and real estate markets makes returns on dollar denominated assets look particularly attractive to global investors vis-a-vis returns on assets in other countries.  An example of this strong global demand was the recent purchase by Chinese investors of the Waldorf Astoria Hotel in Manhattan for almost $2.0 billion dollars, one of the three transactions totaling more than $1 billion in the past year.

While Manhattan remains the largest market in terms of volume and commands the highest price per square foot, the DC commercial market has also seen a sharp rise in mega deals where sales prices for properties exceed $200 million.

Large DC commercial transactions in the past 12 months, by sales price


Highlights of recent commercial sales transactions in DC

    • The ten largest mega transactions, with sales prices exceeding $200 million, accounted for over $3.1 billion of sales activity, almost half of the total value for all large commercial transactions.
    • Among these transactions was the sale of 800 17th St. NW. This transaction set a record price of over $1000 per square foot, pushing DC office prices closer to the $1300 per square foot on average paid in Manhattan.
    • Just below this threshold there were 13 transactions with sales prices exceeding $100 million, which contributed to over $1.6 billion dollars in sales volume.
    • Rounding out the list of large DC transactions were 36 sales between $25 million and $100 million which added another $1.8 billion to sales activity

Highlights of mega commercial sales transactions in the nation



MANHATTAN HOTEL $1,791,829,000 $1.4 Million (per key)
730 5 AVENUE MANHATTAN RETAIL $1,775,000,000 >4000 PSF
1801 K STREET DC OFFICE $445,000,000 $790 PSF
800 17 STREET DC OFFICE $392,000,000 $1075 PSF

What exactly is the data? DC sales data are from the DC Recorder of Deeds. NYC data was provided by the New York City Department of Finance.

How Housing Costs Stack up in DC Compared to Other Expensive Housing Markets: Do Lower Property Taxes Lead to Higher Home Prices ?

In this post we analyze how the two main components of housing costs-mortgage payments and property taxes vary in some of the nation’s most expensive housing markets. This approach has the advantage of capturing both the overall underlying costs of housing and the interaction between property taxes and home values.

By focusing on costs, rather than home prices, the data better reflects the fact that one of the main reasons for the sharp increase in home prices that has occurred over the past 20-30 years is the significant drop in interest rates that occurred over this time frame.  As a result of the drop in mortgage rates from almost 10 percent in 1990 to 4 percent in 2015, the payment on a home that cost $200,000 in 1990 is equivalent to a payment on a home valued at over $400,000 today. Here’s what the data looks like for all homeowners:

Annual Homeowners Costs: All Homeowners


  • San Francisco topped the list for all homeowners with annual housing costs exceeding $36,000. The hyper gentrification that has occurred in this market as a result of the tech boom has caused overall housing costs to exceed those in Manhattan.
  • District property taxes were lower than in most of these other expensive housing markets with the exception of Brooklyn.
  • The District’s lower property taxes were offset by somewhat higher mortgage payments that brought overall housing costs in line with the other expensive markets, excluding the top two markets of Manhattan and San Francisco, which are set apart from the rest of the nation.
  • Housing costs in Brooklyn were lower compared to these other markets. Brooklyn comprises a vast housing market of more than 2 million residents and includes both very expensive and relatively moderate priced homes.

Differences in housing costs can arise because households have different median incomes. As a result, median home prices and property taxes would also vary.  Manhattan and San Francisco topped the list for both median income and median home price, with median income exceeding $70,000 and median home prices north of $700,000 in both these markets.  To better control for differences in income, we looked at the data in greater detail for only those filers with household income in excess of $200,000.

Annual Homeowners Costs: Homeowners with Income Greater than $200,000


  • Manhattan topped the list with annual homeowner costs of $55,000 narrowly exceeding costs in San Francisco. Homeowners in San Francisco paid higher mortgage costs compared to Manhattan residents, but property taxes were significantly higher in Manhattan. This could be the result of Proposition 13, which imposes strict limits on increases in property taxes in California.
  • Excluding these top two markets, homeowner costs in the District were second highest, after Los Angeles. As was the case for overall homeowners, lower property taxes in DC compared to most of the other markets were offset by higher mortgage costs.

What can we conclude from the data:

  • The drop in mortgage rates to historic lows that occurred over the past 20-30 years that contributed to sharp increases in home prices is likely behind us. Going forward interest rates are likely to rise, and increases in home prices could be more moderate and reflective of increases in household income.
  • The data also suggests that lower property taxes can lead to higher home prices and vice versa. How this relationship plays out among existing landowners, and property owners versus new homeowners is an interesting question for policymakers and residents alike who also have to weigh in how property taxes affect funding for public services like education and safety which in turn can affect property values.

What exactly is the data? Data in this report is from the 2012 IRS County Statistics of Income. Most homeowners in these expensive housing markets can itemize their mortgage payments and property taxes on their income taxes.  The IRS data has the advantage of capturing most homeowners in these markets rather than relying on sample data from the American Community Survey. The data also better reflects actual property taxes paid compared to published statutory rates.

Economic literature on this topic can be found in “The Effects of Property Taxes and Local Public Spending on Property Values: An Empirical Study of Tax Capitalization and the Tiebout Hypothesis” by Wallace E. Oates

Bob Zuraski contributed to this post

Is CEO pay the major cause of income inequality in the District? Increasingly the corporate ladder you’re on matters more than where you are on the corporate ladder.

Much of the attention on the causes of growing income inequality has been focused on the difference in pay among the very top earners and the pay of other employees.  CEO and executive pay is an important factor in explaining growing income inequality both nationally and locally.  However it is the widening gap in median pay between different occupations that explains why income inequality in the District is higher compared to other parts of the nation.  In 1975 a salesperson working in the District, in a middle to upper middle position in her/his career, would likely have been a middle class resident.  Today she/he is considerably less well off than an entry-level attorney. We will discuss some of the implications of this growing disparity but we begin by looking at the data to illustrate this  point.

Ratio of Wages for Top Ten Percent of Earners vs. Bottom Ten Percent: 1



As shown above, the top 10 percent of earners in the District across all occupations made more than six times the lowest ten percent. The disparity in earnings in the District was higher than any of the 10 largest states, and in fact was higher than all 50 states.

When we looked at this same ratio of wages of top earners compared to low earners within individual occupations, the results were significantly different.  Interestingly for high paid occupations (like legal, and business  and financial occupations), the ratio of wages  for the top 10 percent of earners  compared to the bottom ten percent was lower in DC than the ten largest States.

Ratio of Wages for Top Ten Percent of Earners vs. Bottom Ten Percent:



For example, the top ten percent of earners in business and finance occupations in the District made 2.8 times the lowest 10 percent.  In New York, this ratio was 3.6 percent.  The same result, the lower disparity among pay, was also true for legal occupations.  This may be due to the larger presence in DC of legal occupations within the government sector, where salary ranges even for the highest level are narrower compared to the private sector.

This does not imply the top ten percent of earners in the District made less than their counterparts in other states. What this says is that the relative difference between top earners and low earners for these high paying occupations is less pronounced in the District compared to other States.

Ratio of Wages for Top Ten Percent of Earners vs. Bottom Ten Percent:



For low paying occupations such as food services, the earnings ratio of high paid earners to low paid earners was somewhat higher in the District compared to other states, but not significantly different for other  low paying occupations like sales.

Overall, for most low paying occupations, the data does not indicate that the difference between the top earners and the lowest paid earners in the District is higher than in other parts of the nation.

So why is income inequality in the District greater than in other parts of the nation?  The cause is not the greater difference in pay within any particular occupation (along the corporate ladder).  Rather it is due to a greater disparity in pay across industries (which corporate ladder you’re on). The data below compares median pay in high-paying occupations such as legal, to low-paying occupations such as sales. In DC the average person employed in legal occupations earns almost 5 times the pay of an average employee in sales. In California and New York the ratio is about 4 times and in North Carolina and Texas the ratio is 3 times.



The larger disparity in median pay between high paying occupations and low paying occupations explains in large part the District’s higher income inequality relative to other parts of the nation.  This has many implications. The likelihood that someone in a low paying occupation can attain middle class status by moving up their career ladder is lower in DC than other parts of the nation.  The skills and experience that in the past led to middle class status are more limited today to certain occupations. Without these skill sets it is unlikely that a person can attain middle class status even by rising up the corporate ladder through work experience.

What exactly is the data:

Data in this report is from the BLS Occupational Employment Series.  Probability sample panels of about 200,000 establishments are selected semiannually. Total 6-panel un-weighted employment covers approximately 78 million of the total employment of 136 million.

One limitation of this data set is that it does not cover non-production bonus pay such as such stock options. These types of compensation could account for an increasing proportion of pay at the high-end of the income distribution, particularly for the top 1 percent which would include CEOs. The occupations that pay bonuses tend to be clustered in high paying occupations such as legal services, finance and information services, so that the general results here- that the corporate ladder you’re on increasingly matters more than where you are on the corporate ladder still hold.

Bob Zuraski contributed to this post

Innovation in the District: Why is DC’s digital divide larger compared to other cities? What factors account for the divide?

In a previous post we examined patent data as a measure of innovation that occurs in the District and in other major cities. Using this measure, we observed that while the District was among the leading tech centers in the nation, it still lagged behind some of the top tier cities, particularly those on the West Coast.  The data also suggests that a strong eco-system supporting innovation was likely to be an important factor in explaining where innovation occurs.

Following up on this research, we look at another aspect of the technology eco system– internet access among households.  It’s difficult to imagine how individuals can be competitive in today’s tech world without access to internet in their homes. This is particularly true given the very young age of tech entrepreneurs and the fact that they are  likely to develop crucial  internet and computer skills early on in their lives, at home and in school, rather than in the workplace.   A study by Hans Kuhlemeier  and Bas Hemker, The impact of computer use at home on students’ Internet skills, found that “home access to e-mail and the extent to which students use the home computer for surfing, e-mailing, chatting and text processing were found to be substantially related to Internet and computer skills (taking into account the effect of several background characteristics of the students).”

Using  U.S. Census data we examine the digital divide that exists among households of different income levels and whether income and other demographics factors such as age and race explain differences in internet availability across major cities.

Here’s what the data shows:

Households without Computers and Without Internet Access:


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

Overall computer and internet availability for District households was comparable to the national average, although DC lagged behind several competitor cities such as San Francisco, Austin and Portland.

The data shown above does not reflect the large differences in internet availability among households at different income levels. Other research has highlighted the digital divide that exists between low income households and high income households.   Here we compare DC households to households in other competitive tech cities with the same income.

Percent of Households Without Internet Access: Incomes Less than $35,000


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


  • The majority (53%) of households in the District with incomes below $35,000 lack internet access at home.
  • The District lagged behind the national average and most other major cities by this measure. In Austin and Seattle about one-third of low income households lacked internet access, a far lower share than the District.
  • Even in expensive housing markets such as New York and San Francisco, internet availability among low income households was higher compared to DC.

Percent of Households Without Internet Access: Incomes between $35,000 and $75,000

3Source: U.S. Census Bureau: American Community Survey, DISTRICTMEASURED.COM

  • While internet access increased significantly for households between $35,000 and $75,000, almost 30 percent of households in DC in this income range still lacked internet access.
  • For the West Coast cities and Austin, the percentage of households lacking internet access was about half as a high as in the District.

For households with incomes greater than $75,000 (shown below) the situation is very different.

Percent of Households Without Internet Access: Incomes  Greater than $75,000


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

  • Nationwide only 8 percent of households with incomes greater than $75,000 lacked internet access at home. For the District the percentage was 7%.
  • There was far less variation among major cities for this income group, the lowest percentage was on the West Coast, the highest was for New York City and Philadelphia.

While internet access and income are strongly correlated, research also suggests that other demographic factors such as age and race also account for differences in internet use.  A Pew Study  found that seniors and minorities were less likely to use the internet.  We looked to see whether age and race could account for the large difference in internet access between DC and San Francisco.   We found that San Francisco had a much higher concentration of low income households among seniors compared to DC and that age or income alone did not account for the lower internet availability in DC.  A larger presence of minorities explained some, but not all, of the gap between DC and San Francisco, after controlling for income and age. Other non-demographic factors, including innovative private and not for profit outreach efforts targeted at low income households, cultural factors and other social factors are also likely to help explain the digital divide.

What exactly is the data?

Data are from the U.S. Census Bureau:  2013 American Community Survey. Data are based on a sample and are subject to sampling variability.

An abstract of the study “The impact of computer use at home on students’ Internet skills” by Hans Kuhlemeier  and Bas Hemker can be found by clicking here.

Robert Zuraski contributed to this post

Income differences among spouses in the District are still significant.

In this post we explore whether spouses have similar income characteristics. To examine this we analyzed DC income tax data for married couples and domestic partners who file a combined separate income tax return. This fling status allows us to compare the incomes of each filer separately and determine whether their incomes are similar.

We examined income differentials among couples/partners by computing the ratio of their respective incomes.  We looked at three levels of income differentials, a 50 percent differential, a 25 percent differential and a 10 percent differential.  For example, a couple where one spouse has income of $50,000 and the other has income of $100,000 would have a 50 percent differential, whereas a couple where one spouse has income of $90,000 and the other $100,000 would have a 10 percent differential.

Here is the data:

Income differences among spouses:1

 Source: Office of Revenue Analysis, DISTRICTMEASURED.COM


  • The lowest income differential is for couples making between $100,000 and $200,000, who account for the majority of married filing combined separately filers. The figures were almost identical for those with aggregate income between $200,000 and $300,000.
    • 61 percent report an income differential of less than 50 percent
    • 36 percent report an income differential of 25 percent or lower.
    • 21 percent report an income differential of 10 percent or lower.
  • As expected, income differentials are largest among couples with high aggregate incomes- above $500,000.
    • Only 28 percent of couples with incomes between $500,000 and $100,000 have an income differential lower than 50 percent.
    • For millionaires, the comparable figure is 16 percent.
  • We were surprised to see that even among millionaires, 9 percent have an income differential lower than 25 percent, and 5 percent report an income differential that is lower than 10 percent.
  • We tested to see whether these results changed for couples who had dependents. The results were very similar for all income ranges except the lowest. This suggests that for low income couples the presence of a dependent could result in one of the spouses taking on a part-time job to take care of dependents. Conclusions regarding whether a spouse leaves the workforce altogether in the presence of a dependent cannot be drawn from this data. (See note in data section below)
  • The only significant difference from the results shown in the table above occurred when we limited the results to seniors. We found that only 44 percent of seniors reported an income differential lower than 50 percent.
  • The tax data does not provide information on the gender of the spouses. It merely identifies a primary and secondary filer. For the majority of filers, 63 percent, the primary filer had a higher income than the secondary filer.

Overall the data confirms that there are still significant income differences among spouses. About 50 percent of all couples report an income differential greater than 50 percent. The differential is less pronounced for spouses with aggregate income between $100,000 and $350,000. Still almost 40 percent of spouses in this income range report an income differential of more than 50 percent.

What exactly is the data?

Data is from the 2012 DC personal income tax filings and refers to taxable income for married and domestic partners filing combined separate.  The income tax data has the benefit of including more filers than sample data from the American Community Survey which includes only about 8,000 overall households for all filing types.  There are however some important limitations to the tax data. The analysis excludes married filers who file taxes jointly. These filers tend to be concentrated among lower-income married couples or couples where only one individual has income. Because couples who file jointly do not report their incomes separately we cannot compute their income differential.  In the case of one-earner couples, the numbers shown in the table above would understate the income differential particularly for the lowest income range.