DC’s unemployment as been increasing over the past six months, with the rate rising to 6.4% in July

More DC residents are working, but resident employment growth has not kept up with that of the labor force

According to the US Bureau of Labor Statistics (BLS), unemployment in the District of Columbia has been rising over the past six months. Seasonally adjusted unemployment rose from 22,376 in January 2017 to 25,706 in July 2017, an increase of 3,330 (14.9%). The rate of unemployment rose from 5.7% in February to 6.4% in July.

graph 1 sept 2017graph 2 sept 2017

The rise in unemployment does not mean, however, that the number of employed DC residents fell. To the contrary, there were 4,313 (1.2%) more DC residents working in July 2017 than there were in January. Unemployment rose because for the past 6 months the increase in jobs for DC residents did not keep up with the even faster growth in the DC labor force.

table 1 sep 2017

As explained below, unemployment can be viewed as the difference between the labor force and resident employment. Unemployment goes down if resident employment increases more than the labor force. This is what happened from July 2016 to January 2017. At that time unemployment decreased by 1,324, following the trend of the prior two years. Unemployment goes up if resident employment increases less than the labor force. This is what happened from January 2017 to July 2017 when the labor force increased more than twice as much as in the prior 6 months, and unemployment rose by 3,330.

Unemployment is defined by BLS as people without jobs who are looking for work. This is calculated each month based on a monthly survey of a sample of households. The survey also counts people who are working. The labor force is then estimated by adding together the number working and the number who are unemployed. Unemployment can therefore be viewed as the difference between the labor force and resident employment, and the unemployment rate expresses unemployment as a percentage of the labor force.

The following charts and table show that for most of the past 3 years DC’s resident employment has grown faster than the labor force, with the consequence that unemployment and the unemployment rate steadily declined. The data does not explain why unemployment has started to rise in recent months. The reasons the labor force can grow more than resident employment include arrival in the city of more workers looking for jobs and existing residents returning to the labor force because of improving prospects of finding work. Whatever the reasons, DC’s unemployment rate over the past 6 months rose from 5.7% to 6.4% as the US rate was falling from 4.8% to 4.3%.

Table 2 sept 2017.PNG

graph 3 sept 2017graph 4 sept 2017

graph 5 sept 2017

About the data. The data discussed here are labor force statistics prepared each month for the US and the states by the US Bureau of Labor Statistics (BLS). The data are derived from household surveys, and are subject to sampling and reporting errors as well as changes in underlying demographic information that is taken into account by BLS in making the estimates. In practice, labor force is constructed by adding together those who say they are working and those who say they are unemployed (this is, not working but looking for work). All calculations are from seasonally adjusted data. The data reflect revisions to the original July 2017 estimates made by BLS in its August report, but the data are also subject to further revision by BLS. Seasonal adjustment is the method BLS uses for removing seasonal elements (such as school graduates seeking to enter the labor force or holiday period fluctuations) from monthly labor market statistics. This is done to reveal underlying trends and cycles in the data.

A version of this blog appeared in the September 2017 OCFO report District of Columbia Economic and Revenue Trends.

Metrorail vs. Uber: Travel Time and Cost

With Uber and other ridesharing services becoming a common transit option for some D.C. residents, we wanted to get a sense of when someone might substitute an Uber trip for a Metrorail trip. To do this, we plotted data on travel time and cost, creating a visualization that shows whether Uber or Metro is faster, and at what cost, for 114 different trips between Metro stations. By adding in the time it takes to wait for a Metro train or Uber, walk to the Metro, or sit on a delayed train, we can see how a person’s decision might change depending on their circumstances.

The trips we analyzed include trips between the city and the suburbs as well as trips within the city. In our visualization below we define a long trip as one that’s more than five Metro stops.

(Click to interact with the graph and change the assumptions.)main graph

We found that for longer trips between the center of the city and the suburbs, Metro tends to be both more cost-effective and quicker than Uber. But for trips within the city that require a Metro transfer, Uber is often quicker than Metro, especially when Metro wait times are long, like on weekends, or when there are delays. While Uber’s regular service tends to be much more expensive than Metro, Uber Pool makes some Uber trips nearly as affordable as Metro.

The data for our analysis comes from the Metro trip planner, the Uber app, and Uber Movement. All travel times are from the weekday evening rush hour, which likely gives Metro an advantage on trip times. We pulled our data after SafeTrack ended.

Short Metro wait times and no delays make Metro an attractive option

The graph above shows a scenario that favors Metro, since there are no delays and relatively short wait times for trains (3 minutes). Under this scenario, Metro provides a trip as fast or faster than Uber for 67 of the 114 trips we looked at. Metro is especially efficient for longer trips from downtown to the suburbs that do not require transfers. A trip from Metro Center to Bethesda, for instance, takes 33 minutes, and that includes time to wait for the train and walk to and from the station. An Uber takes 47 minutes, and comes at a far higher cost. Meanwhile, Uber tends to be faster, even during rush hour, for trips within the city requiring transfers. Going from Union Station to Minnesota Avenue or from Columbia Heights to Cleveland Park, for instance, is about 20 minutes quicker in an Uber than on Metro, even when Metro is running efficiently, without delays or long headways.

With Metro’s spoke-and-hub configuration, it’s not surprising that trips requiring a transfer that have origins and destinations relatively close to each other are quicker in an Uber than on Metro. Metro accounts for this, and has bus routes connecting the “spokes” of its rail system. You can get between the Union Station and Minnesota Ave Metro stations using the X2 bus. To get from Columbia Heights to Cleveland Park you can take the H4 bus. Some folks have their own work-arounds, and might bike between these locations. All of this is to say that even if Uber is faster than Metro for trips with a transfer, there are other modes of transit a person can use to make this sort of trip.

Long Metro wait times give Uber a large advantage

The scenario under which the case is stronger for Uber is when Metro has a delay or a long wait for the train, like what you’d encounter on a weekend or at night. If we increase the wait time for each train from 3 minutes to 10 minutes, as we do in the graph below, Uber becomes quicker for the vast majority of trips (99 out of 114). For example, the trip from Columbia Heights to Eastern Market is 9 minutes faster in an Uber when there is a 10-minute wait for a train. With a wait of only 3 minutes, Metro would be faster for this trip.

long waits

Factoring in cost

But what about cost? You can save 9 minutes by taking an Uber from Columbia Heights to Eastern Market when there are 10 minute headways, but the Uber ride will cost you about $10 more than Metro. For some people the time savings will not be worth the cost. But with Uber Pool, which allows you to share a ride with a stranger whose route roughly coincides with yours, you can get rides at a reduced cost.

If you take an Uber Pool for the trip between Columbia Heights and Eastern Market, for instance, the ride will be about $1 more than on Metro. The graph below shows that with Uber Pool, 74 of the 114 trips we analyzed have Uber fares that are no more than $5 above what you’d pay on Metro for the same trip.

With the reduced price there is a trade-off: we assume a ride in an Uber Pool will take 5 minutes longer than in a regular Uber.

uber pool.png

It is unclear how long Uber prices will remain this low. Several news outlets have reported that Uber subsidizes its rides with money from investors, meaning current fares might not reflect the full cost of a ride.


We want to caution that we are not sure if our findings are representative of the entire Metrorail system. We looked at 114 trips, but there are over 4,000 possible trips you could take on Metro. The trips we included in our analysis are listed in the graph below. You can use the graph to search for a particular station and see whether Uber or Metro is faster for a trip involving that station, given assumptions you enter about wait times. The trips we included fall into two categories: 1) trips from either Metro Center or Chinatown to all other stations in the system, with the origin being the station that makes the trip one without a transfer (though we did exclude trips of only one stop in length); or 2) trips within DC city limits requiring a transfer, with one end of the trip including a jobs center (Foggy Bottom, Navy Yard, etc), nightlife center (Shaw), or other heavily-used station (Columbia Heights). In the second case we purposefully included some trips for which we thought Uber would be faster than Metro in order to broaden the range of differences in trip times.

(Click to interact. You can use this graph to search for particular stations.)bar graph

What exactly is this data?

You can download the data we used for this post here. Below we describe how we gathered the data.

Metro: Metro trip times come from the Metro trip planner. We did not include in the trip times the time it takes to wait for a train or to transfer trains, since we made our own assumptions about wait times. We also used the trip planner for the cost of Metro trips. The trip times and costs are for weekdays at 5:30pm. We pulled data from future dates, meaning the times do not capture unexpected delays, and the trip times did not appear to be affected by planned track work. The dates we entered to pull the data were after SafeTrack ended.

Uber: Uber trip times come from Uber Movement. The times are the three-month average for April, May, and June, 2016, during the weekday evening commute. It is unclear whether the times from Uber Movement include Uber Pool trips, which we believe could take longer than Uber X trips. (We asked Uber whether Pool times were included, but did not receive a response.) When we accessed Uber Movement, the tool was in Beta form. It is now no longer in Beta and is available to the public.  We gathered Uber cost data on the Uber app between 5pm and 6pm on the following dates: July 5, July 6, and July 19, 2017. To the best of our knowledge, prices were not surging on these dates nor was there rain during the times we collected the cost data.

Calamari, Cocktails, and Cars: A Glimpse into the District of Columbia’s 10% Sales Tax Collections

In the District of Columbia in 2015, a whopping $361.46 million was generated by the 10% sales tax rate, which is one of five rates used to tax the retail sale of various goods and services in the city. This rate, commonly known as the “Restaurant Tax”, encompasses the sale of not only restaurant meals, but also other prepared foods, alcohol/liquor sales for on-premise consumption, and the renting of vehicles. With so much revenue generated by this tax rate (over 35% of total DC sales tax revenue), we set out to examine the business and economic trends affecting the city’s 10% sales tax collections. To do so, we analyzed the annual sales tax receipts of all businesses in the city for all years from 2005 to 2015. We then focused on the top 100 taxpayers in the 10% tax rate during each year and categorized each taxpayer into one of nine sectors to conduct further analysis: Fine Dining, Casual Dining, Fast-Casual, Fast Food, Food Service/Catering, Supermarket, Hotel, Transportation, and Other, utilizing them to help draw general conclusions about the business outlook of the city as a whole. For example, we found that while there has been significant growth and development of the restaurant industry in the city, when we adjust for inflation between 2005 and 2015 for the top 100 taxpayers, many sectors actually see declining revenues in real terms. The full report can be found here.

I. DC’s 10% Tax Rate: A Sector Analysis

First, we examined each of the top 100 taxpayers within the scope of their sectors in order to track the changes in each aggregate. Overall, we found that all but two of the sectors (excluding “Other”) increased their revenue from 2005 to 2015 (Table 1). Surprisingly, this growth was led by two of the more “unusual” sectors in the city’s “Restaurant Industry”: transportation and supermarkets. While comparatively small in 2005, these two sectors have grown tremendously, so that they outpace even Fast Food in sales tax revenue in 2015. While we aren’t too sure why car rental and car sharing services have done so well, especially since there are plenty of options to travel around DC, including taxis, the Metro, the Circulator bus, and many ridesharing options, this growth may suggest robust demand from 1) the city’s business sector; and 2) the city’s residents, who may rent vehicles for short trips and excursions out of the city given the trend for a growing number of new residents to choose to forego car ownership.


The two categories that experienced negative revenue changes from 2005 to 2015 (again excluding “Other”) were Fine Dining and Fast Food. This finding may be the result of the growing number of city restaurant patrons becoming more health conscious and/or more price sensitive. Therefore, they are largely turning away from fast food, which is considered less healthy, and fine dining, which can cost hundreds of dollars per meal. These customers are likely then turning to restaurants in the middle of the “dining spectrum”, fueling growth in fast-casual and casual restaurants, which offer relatively healthy and lower-priced dining options.


After adjusting all figures for inflation according to the District of Columbia Consumer Price Index, however, the figures tell a much grimmer story. Instead of the top 100 business mostly seeing increased business and consumer activity, most sectors are not able to keep pace with inflation, with only four sectors able to do so (Figure 1). Of these four, only two (transportation and supermarkets) were able to increase revenue at a rate more than 3% faster than inflation, showing the difficulty most companies face while trying to achieve growth.

II. Business Size vs. Tax Impact

As displayed in Figure 2, the top quintile accounted for over 80% of the market share in 2005, while the lowest quintile accounted for a meager 0.2%. Even though in 2015 the top quintile still accounted for the lion’s share of the overall market share (76%), revenue is slowly being redistributed towards the lower quintiles over time, with the share of all four lower quintiles increasing from 2005 to 2015, suggesting an increasingly diverse and competitive marketplace in the city.


More clearly displayed in Figure 3, the vast majority of growth in sales tax receipts is visible in the middle three quintiles, which include taxpayers with liabilities that range from $4,500 to $150,000 in 10% sales tax in 2015. Each of these middle quintiles grew over 30% from their original market share, equating to $80 million in total tax payment growth. Additionally, we see an 18.5% growth in the number of businesses filing a 10% sales tax within the city from 2005 to 2015, signaling a positive environment for small/mid-sized business development and growth.


III. Business vs. Household Tax Impact

Every day, hundreds of thousands of people commute into the nation’s capital, and many more are in the city on business trips, for leisure, and for other reasons. Therefore, unlike most taxes, such as the individual income tax and property tax, the 10% sales tax is not drawn entirely from District of Columbia residents. Rather, it stems from the entire District’s population at any given time, which during working hours is approximately 79% larger than the number of DC residents, according to the US Census Bureau as of 2015. Thus, we wanted to estimate the proportion of the tax falling upon business consumers compared to DC households.

To do so, we grouped four of the aforementioned categorizations as “Business”: Fine Dining, Hotels, Food Service/Catering, and Transportation, while we grouped another four as “Household”: Casual Dining, Fast-Casual, Fast Food, and Supermarkets. We defined the Business industry as sectors where a majority of sales would likely stem from businesses and/or governmental organizations, while we defined the Household industry as sectors where the majority of sales would likely stem from individuals’ retail purchases.


We found that among the top 100 taxpayers, on average, sales tax revenue generated by the business sector represented 62% of total sales, while the household sector only generated 32% of total sales. Since such a large proportion of the tax burden falls upon the business sector, it is clear that rather than individual purchases accounting for the majority of 10% tax revenue, the revenue is largely generated from business purchases of catering, hotel, car, and fine dining services.

IV. Conclusions

By way of our sector analysis of the top 100 taxpayers from 2005 to 2015, we first found that although all but two of the classifications (excluding “Other”) saw nominal revenue increases over the time period studied, this has largely been due to inflation: after an inflation adjustment, only four sectors saw real revenue increases, while only two saw increases significantly above the inflation benchmark.

Next, although we observed the top quintile of businesses in the city accounted for over 75% of the 10% tax revenue during all times between 2005-2015, we have noticed a gradual redistribution of restaurant retail activity towards the lower quintiles, with the vast majority of growth concentrating in the middle three quintiles. This, coupled with an 18.5% increase in businesses paying a 10% tax, suggests that small/mid-sized business growth is prominent in the District.

Finally, even though the city is experiencing steady population growth, gentrification, and other factors that should increase the tax payments of the household sector, the majority of the 10% tax burden still falls on the business sector, with their overall market share actually increasing during the time period studied. This is can actually be viewed as a positive, as many businesses from regions outside the District are contributing hundreds of millions of dollars every year towards D.C. tax revenue, instead of the tax burden entirely falling upon city households and residents.

About this data: Sales tax data are from confidential DC monthly tax filings, compiled by the Office of the Chief Financial Officer, Office of Tax and Revenue. Inflation adjustment data are from the DC Fiscal Policy Institute. Population adjustment data are from the Office of the Chief Financial Officer. Commuter data are from the United States Census Bureau.

The Elephant in the Boom: Global, U.S., and District income inequality in an era of general economic expansion and globalization

The “Elephant Chart”

Across most developed countries, including the United States, income growth has stagnated for low and middle class workers over the last several decades. Real GDP per capita for the U.S. grew merely 36 percent cumulatively during the 20-year period from 1988 to 2008. This period also saw some of the greatest growth stories in world history for China and India, as Chinese and Indian real GDP per capita, as measured in constant international dollars, has increased by 560 percent and 230 percent, respectively, during the same 20-year period, according to data from World Bank.

The stark contrast in global income growth between developed and developing nations is captured succinctly by the graph known as the “Elephant Chart,” as shown in Figure 1. This much-discussed chart was produced by the former World Bank economist Branko Milanovic last summer. We borrowed the chart from Branko Milanovic’s blog.  The chart ranked the world’s households from the poorest 1% to the richest 1%. At each percentile, the chart shows the growth in income between 1988 and 2008, an era of increasing globalization.

The chart, nicknamed the “elephant chart” because of its peculiar shape, shows that in the last few decades the “global middle classes,” mostly from China and India (point A) and the world’s elite (point C) have gained income significantly in the era of globalization, while income for the middle classes in the richest countries (point B) have stagnated. According to Milanovic, from 1988 to 2008, relative income growth was 75 percent for the developing world middle classes and 65 percent for the global top one percent, but only 0-5 percent for the rich countries’ middle classes. Despite acknowledging that correlation is not equal to causation—many economists nevertheless point to the chart as proof that globalization allowed developing countries like China and India to grow at the cost of middle class workers in rich countries.

Figure 1: Change in Real Income Between 1988 and 2008 at Various Percentiles of Global Income Distribution (Calculated in 2005 International Dollars)image001

Source: Branko Milanovic’s blog

How does the U.S. compare?

The “elephant chart” is an interesting and remarkable chart. One may wonder how the contours of inequality for the U.S. and Washington DC look when compared to the “elephant chart.”  Using the Public Use Microdata Sample (PUMS) data from the Census Bureau, we have created our own “elephant charts” for the United States and for the District of Columbia between 2001 and 2015. We will also compare the DC chart based on Census data, with the chart based on personal income tax data collected by the District government. We chose Census data for this specific period mainly for comparison purposes because DC income tax data is available only for this period.

The pattern of inequality in the United States is much different from the world’s. Figure 2 illustrates something that is all too familiar: for the past 15 years, which includes a mild recession and a financial panic, the country’s middle class and the poor have seen their incomes fall. In fact, households in the bottom 75 percent of distribution have experienced income losses of 8 percent on average during the 15 years from 2001 to 2015. Households in the top 25 percent of distribution, on the other hand, have experienced real income gains averaging 5 percent. The upper-class households in the top 5 percent of the distribution have been doing particularly well, with income gains averaging about 14 percent during the same period. The pattern does remind us of the so called “Matthew Effects,” where the rich get richer and the poor get poorer.

Figure 2: Real Income Gains Between 2001 and 2015 for U.S. Households at Various Percentilesimage002

            Source: U.S. Census Bureau

How about the District?

In contrast to the income trend nationally, the majority of households in Washington DC saw their incomes rise, especially among middle-income percentiles. The DC contour, as shown in Figure 3, is “elephant-like:” Middle class households and top earners in the District have experienced the largest income growth.

Figure 3: Real Income Gains for DC Residents at Different Percentiles Between 2001 and 2015image004

            Source: U.S. Census Bureau

Since the Census data for DC is survey based on a sample size of only about 3,600 DC households, the sampling error is relatively large. On the other hand, the number of tax filers in DC averages about 360,000 per year.  The blue line in Figure 4 shows the distribution of before tax income growth for DC based on DC tax filer data. Consistent with the chart based on census data, most DC residents have experienced income gains even after taking inflation into consideration. This is a clear contrast with the national pattern. One notable feature of Figure 4 is that residents at around the 21st to 25th percentiles experienced stagnant income growth. Low income tax filers from the 5th to 20th percentiles fared better. We believe this may be attributable to DC minimum wage policy. Since 2001, the DC minimum wage has grown much faster than inflation. As shown in Figure 5, the DC minimum wage had increased by 71 percent while the cost of living had increased by 41 percent. Thus, residents who have benefitted from the rising minimum wage (those in the 5th to 20th percentiles) have seen their income growing faster than residents working at wage rates just above the minimum wage (those in the 21st to 25th percentiles).

Figure 4: Before and After Tax Real Income Gains for DC Residents at Different Percentiles Between 2001 and 2015 Based on Income Tax Dataimage006

            Source: Office of Tax and Revenue

Figure 5: DC Minimum Wage VS the Consumer Price Index (CPI)image008

            Source: U.S. Department of Labor

The red line in Figure 4 shows an income growth pattern after adjusting for the District government’s tax policies such as taxable deduction, exemption and refundable and nonrefundable tax credits. The area between the blue line and the red line represents increase in income growths that was due to tax policies. The poor apparently benefited significantly from this tax policy. The DC earned income tax credit (EITC) did most of the job in helping the low-income residents (at or below the 30th percentile), as the District’s EITC credit has increased from 25 percent of the federal credit amount in 2001 to 40 percent of the federal amount since 2009.  For reference, Table 1 shows how much income DC tax payers earned in 2015 at certain percentiles both before and after tax.


Source: Office of Tax and Revenue

Table 2 takes another look at how taxpayers at different income percentiles fared from 2015. The share of total income for the bottom 30 percent is much lower despite their relative increase from 2001 to 2015. The bottom 30 percent taxpayers earned only 5 percent of total before-tax income in 2001, and this percentage dropped to 4 percent in 2015. The DC Income tax schedule did help the taxpayers in the bottom 30 percent by maintaining their after-tax income share at 5 percent from 2001 to 2015.

The middle class, typically defined as households with income between the bottom 30 percent and the top 20 percent, fared slightly worse, with the before-tax and after-tax income shares declining by one percent and two percent respectively between 2001 and 2015.

The top 20 percent upper class households fared much better than the middle class by gaining both before-tax and after-tax income shares. The top 1 percent received a lion’s share of 23 percent of total before-tax DC income in 2001, and this percentage increased to 24 percent in 2015. The top 1 percent actually benefited more from the DC tax policy as their share of total after tax income increased from 20 percent in 2001 to 23 percent in 2015.

Table 2. Income as a percentage of total taxable income for taxpayers at different percentiles of income distribution


In conclusion, we have gained substantial knowledge about inequality in DC and in U.S. from reproducing the “elephant chart” for the period between 2001 and 2015. We found that households in Washington DC on average experienced faster income growth than their counterparts in the nation for this period. Although the top one percent earners in DC and in the U.S. have both done well, the middle class in DC actually experienced real income gains, while the middle class in the U.S. experienced real income losses. We also want to emphasize that DC’s minimum wage and tax policies have helped lower income DC residents gain income faster than their national peers.

What exactly is this data?

Our data on personal income tax is from Office of Tax and Revenue personal income tax returns for tax year 2001 to 2015. The minimum wage and inflation data is from the Bureau of Labor Statistics, U.S. Department of Labor. Public Use Microdata Sample data (PUMS) from the Census Bureau was also used to calculate the distribution of income growths for the U.S. and DC.

Fitzroy Lee, Stephen Swaim and Bob Zuraski contributed to this post.

Job growth in food services in DC has bounced back from last year’s slowdown, but retail has not

New stores and restaurants are tangible evidence of the continued growth of DC’s economy, and these sectors have also been important contributors to employment growth since the Great Recession. Food services and retail combined accounted for 25.2% of the increase in all DC private sector employment in the 7 years since April 2010, when the recession’s effects on DC employment were beginning to wear off. The share of all private sector jobs in food services and retail increased from 11.8% in April 2010 to 13.9% in April 2017—from one in every 8.5 jobs to one in every 7.2.

table 1a.PNG

During most of 2016, however, the amount of increase over the prior year in jobs in food services and retail began to slow down. From December 2015 to August 2016 the annual gain in food services fell from 3,000 per year to just 500. Retail fell from 1,200 to 400. In the fall of 2016, the pace of job growth in food services picked up, but retail continued to slow down. In April 2017 food services employed 2,700 more workers than a year earlier, but retail employed 230 fewer people.


graph 1.PNG

In the years since the Great Recession, there have been ups and downs in food service and retail employment growth. For example, the pace of food services growth hit a high of 3,867 in December 2011, and fell by more than half (to 1,600) a year later. Retail job growth was slightly negative for a brief period in the summer of 2011 and then rose steadily to a gain of 1,433 in June 2014, But the drop in 2016 was the most significant since 2010.

graph 2

Measured as percent change over the prior year, growth in both food services and retail has been greater over most of the post-recession period than for the rest of DC’s private sector. Only toward the end of 2016 did the rates begin to converge.

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Outlook. For the three months ending April, the increase in food service jobs over the prior year, 2,700, was slightly above the average for the past seven years, and the percent change, 5.3%, was slightly below the 5.7% average annual growth over that time. The sector would therefore seem to be poised to add additional jobs if DC’s population, employment, and income continue to grow along the lines of the prior year.

On the other hand, food services employment in the US has been slowing over the past year, falling from a 3.9% rate of growth in April 2016 to 2.2% in April 2017. Although the percentage growth of the sector in DC has generally been above the US average for most of the past decade, DC’s rate of growth last summer declined much faster than the national growth rate. By August 2016 DC’s increase in food services jobs was just 1% while the US rate was over 3%. If the rate of increase in US food services continues to slow or stays at a low level, it remains to be seen whether DC food services jobs can continue to outpace the US as it has over the past several months.

graph 4.PNG


Nationally, the rate of growth of retail employment has fallen over the past year, going from 1.6% in April 2016 to 0.5% in April 2017. DC’s recent decline in retail jobs is thus consistent with national trends, just more exaggerated. For most of the past decade, DC’s rate of growth in retail jobs was well above the US average. Then over the past year DC’s rate of growth fell from 3.8%—more than twice the US rate—to negative 1%. Looking ahead, in addition to factors such as population, employment, and income growth, the retail sector faces the twin headwinds of on-line commerce and checkout automation that could make it harder to sustain job increases in the retail sector.

graph 5.PNG


table 2

About this data. All data is wage and salary employment in DC and the US from the US Bureau of Labor Statistics (BLS). The date is calculated as 3-month or 12-month averages from the monthly series.  The April 2017 amounts used here reflect revisions to the data contained in the May 2017 monthly release from BLS.

Note: A version of this blog appeared in the June 2017 District of Columbia Economic and Revenue Trends, issued by the DC Office of Revenue Analysis.


DC’s median home price, 3 times more than median family income in 1991, is now 5 times more

A measure of house affordability developed by the National Association of Realtors (NAR) relates median family income to median house price. (All further mention of home prices and family income refer to their median numbers.) Before looking more closely at this index, however, we first describe what has happened to home prices and family incomes in DC over the past 25 years. (The home price includes both single family and condominium units.)

Home price and family income in DC. DC’s housing market changed fundamentally after the year 2000. During the 1990’s, home price and family income grew at the same pace. Then, from 2000 to 2006 home prices grew much more quickly than income. The median home price rose from $198,550 to $447,850 over those 6 years, a 125.6% gain, while median family income grew only 34.5%. With the Great Recession the home price fell by 25%, but this only brought prices partway back to the growth path of family income. In the recovery period since 2009, housing prices have modestly outpaced the growth in family income (36.8% compared to 26.3%). (For more details, see the table at the end of this post.)

graph 1


One way to summarize change in DC’s housing market is the ratio of median home price to median family income. The ratio was close to 3 in the 1990’s, and then shot up to about 7 in 2006.4, just before the onset of the recession. During the recession, the ratio did not fall to its previous low level, but only to about 5, where it has remained during the recovery period.

graph 2.PNG

The Affordability Index. As noted earlier, the National Association of Realtors’ Affordability Index compares median family income with the income needed to purchase a median-priced home. The income needed to afford the median house is calculated by assuming (1) 20% down, (2) a 30-year mortgage to finance the balance, and (3) household income at 4 times the amount needed to pay the mortgage. An index over 100 means median income exceeds the amount needed to purchase the median-priced single family or condominium home; an index less than 100 means income is less than what is needed. (An index of 110, for example, means that median family income is 110% of the amount needed to afford the median home.)

The Affordability Index for DC is estimated by Moody’s Analytics. The index was 115.1 for DC in 1991 and fell sharply as housing prices rose after 2000. The index has been close to 100 for most the 7 years since the recovery from the US recession began in 2009, and was 105.4 in the last quarter of 2016.

Given that DC’s median home price increased proportionately much more than family income over the past 25 years, it might seem surprising that the 2016 index of 105.5 is not that much lower than the 115.1 index in 1991. The reason these indices are so close is that interest rates for 30 year mortgages have fallen substantially over the past 25 years. In 1991, the rate was 9.3%; in 2016 it was less than half that—3.9%.

The way the Affordability Index works, a rise in mortgage interest rates, should this occur, would lower the Affordability Index. For example, if the interest rate were to rise by a percentage point (to 4.9%), DC’s index in the last quarter of 2016 would drop to about 95 if the median house price and median family income remained the same. It should be noted, however, that housing prices can also be affected by interest rates. Low interest rates also enable families to pay more for houses, helping to drive up prices. If rates rise and people can’t afford to borrow as much, prices cannot be bid up as high.

graph 3graph 4.PNG


graph 5.PNG

Comparison with the US. In the US as a whole, housing prices and incomes were affected by developments that started in 2000, but the changes were less dramatic than in DC. Prices did not rise as sharply before the recession, and the spread between median home price and median family income never got as large. The result is that deterioration of housing affordability seen in DC has not occurred to nearly the same extent in the US.

  • The ratio of median home price to median family income in the US rose only to about 4 before the recession compared to 7 in DC. The ratio in the US has now fallen back to 3.3, compared to 5.1 in DC.
  • Just before the recession, the Affordability Index in the US was about 120, not too far below what it was in 1991, whereas DC’s fell to 58. The current index in the US (184) is more than 50 points above what it was in 1991, whereas DC’s is now about 10 points less than 1991.

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About this data. Data for median housing prices, median family incomes, and the Affordability Indices for DC and the US are from Moody’s Analytics. Quarterly data for the period from the first quarter of 1991 to the last quarter of 2016 have been used to calculate 12-month moving average values for the years 1991 to 2016. Similarly, quarterly data on the interest rates for 30-year fixed-rate mortgages has been used to calculate 12-month average rates. All index numbers have been calculated using the 12-month average value for the 4th quarter of 1991 as the base value of 100. The National Association of Realtors calculates the Affordability Index for the US and regions of the country. The values of the index for DC were calculated by Moody’s Analytics.

The following tables show values and percent changes over the 1991 to 2016 period for median home prices, and median family income for DC and the US , along with the Affordability Index and the ratio of median house price to median family income.

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Note: A version of this blog appeared in the May 2017 District of Columbia Economic and Revenue Trends, issued by the D.C. Office of Revenue Analysis.


Single-family housing values in the District have risen much more over 25 years than in the metro area or the US

The Federal Housing Finance Agency (FHFA) compiles a quarterly index of single-family house prices for the US, all states (including DC), and metropolitan areas. The index starts in 1991, and is based on how the same properties have changed in value since that time based on sales and refinancing obtained from mortgage and other data sources. (For more detail on the index see “about this data” at the end).

From 1991 to 2016, a 25 year period, DC’s four fold increase is almost twice the increase in the Washington metropolitan area and the US. Over the period, DC’s average annual rate of growth was 5.9%, compared to 3.4% for the metro area and 3.1% for the US.

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Price change patterns were fairly similar from 1991 to 2002, although DC and the metro area initially lagged the US in early 1990’s when DC’s economy was faltering.

When price growth started to pick up after 2002, DC’s increased faster. In the 14 years from 2002 to 2016, DC’s grew 147%, compared to 55% in the metro area and 36% in the US.

DC’s prices also fell less in the recession, and recovery from the recession was much faster. In the 10 years from 2006 (the prior peak) to 2016, DC’s prices gained 37.5%, the US was essentially flat (-1.4%) , and the metro area fell 16.3%.

Why have single-family house prices risen so much faster in DC than in the metropolitan area and the US? The explanation does not lie primarily in changes to general measures of income in the economy. Over the past 25 years DC’s rate of Personal Income growth has been the same as in the US and a bit less than in the metropolitan area. On a per household basis, DC’s income has increased a little faster, but the growth trajectory has still been fairly similar to that in the region and the national economy.

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The major differences between DC and both the region and the US lie in the dynamics of the housing markets that go beyond general measures of income. Since 2002 DC’s housing price index has increased at a much faster pace than average household income. By contrast, recovery in house prices from the recession has not yet been sufficiently strong to catch up with rising average household income in the either the Washington metropolitan area or the US.graph 4 may 1


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Housing market dynamics involve both supply and demand factors. Without trying to fully explain these, it should be noted that DC’s household growth since 2002 has been at a pace comparable to that in the Washington metropolitan area and faster than in the US as a whole. DC’s supply of single family housing, however, is relatively fixed. When growing demand from demographic change and rising incomes meets a relatively inelastic supply, prices can be expected to rise.

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The following table shows the changes in house prices and income from 1991 to 2002, and from 2002 to 2016, in DC, the Washington metropolitan area, and the US.

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About the data. The analysis of housing price in DC, the Washington metropolitan area, and the US is from the Expanded-Data Housing Price Index of single family house prices prepared quarterly by the Federal Housing Finance Agency (FHFA). FHFA calculates the index from repeat sales and refinancing of the same single family properties. It is estimated using Enterprise (federal housing finance agencies), FHA, and real property recorder data licensed from DataQuick. Personal Income and average household income for DC, the Washington metropolitan area, and the US is from Moody’s Analytics.  A version of this blog is contained in the Office of Revenue Analysis publication District of Columbia Economic and Revenue Trends: April 2017.