2017 marks the third time in 70 years DC has had 694,000 residents

From 1950, when it was the nation’s 9th largest city, DC has fared better than most of the then 12 largest cities

The Census Bureau has estimated DC’s population at 693,972 as of July 1, 2017. This is the third time DC’s population has been at that level. The first time was in 1941—on the way up to becoming 900,000 (reached in 1943). The second was in 1976—on the way down to a level of 566,000 (reached in 1998). This third time population is rising again, having grown by 127,000 (22.5%) from the 1998 low point.

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In 1941 employment growth related to New Deal programs and World War II had attracted many people to the city. Population soon soared to 900,000 but started to decline at the war’s end. Even after losing about 100,000 from the war time peak, DC was the 9th largest city in the nation in 1950 with a population of 802,178.

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US population more than doubled from 1950 to 2016, but DC’s declined by 15% over that time, and its rank among cities fell to 21. Measured by percent change in population, however, DC fared better in those 66 years than all but 3 of the 1950 cohort of the top 12 cities.

DC since 1950. The 1970’s knocked DC out of the ranks of the nation’s 12 largest cities. DC held onto the 9th spot in city ranking through 1970 despite population declines in the 1950’s and 1960’s. But the city lost 188,000 people, a 15% decline, in the 1970’s and its rank fell to 15th in 1980. After losing another 65,000, DC was ranked 21st in 2000, and population gains beginning in 2005 were not sufficient to keep the city’s rank from slipping a little further to 24th in 2010. With growth continuing, DC rose to 21st among US cities in 2016, and it is possible that the rise in population that has brought it to the 694,000 mark for the third time in its history may lead to higher relative city ranking as well.

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The 1950 cohort of top cities 66 years later. Much has happened since 1950, but DC has actually fared better than most of the top dozen cities of that year.

  • As a group, these dozen cities lost 1.9 million people, 8.1% of their population, and the share of the nation’s population living in them went from 15.4% in 1950 to 6.6% in 2016.
  • Of the top 12 cities of 1950, 9 lost population. Only New York, Los Angeles, and San Francisco grew.
  • Only four of the cities remain in the top dozen: New York, Chicago, Philadelphia, and Los Angeles.
  • The 121,008 decline in DC’s population was the least of all 9 cities that lost population. Detroit lost the most (1,176,773).
  • The 15.1% decline in DC’s population was also the least of the 9 cities that lost population. Four cities lost 50% or more of their population: Detroit, Cleveland, St. Louis, and Pittsburgh.

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graph 2                                                                              graph 3

Many long time residents of Washington DC are conscious of local developments that have contributed to population declines over the years including such things as urban renewal, flight to the suburbs, public safety concerns, quality of city services, and bankruptcy of the city government. However, all of the large cities of 1950 were affected by enormous changes in the US economy. Industries changed and population shifted to southern and western locations. It is noteworthy that DC’s loss in population in this changing world was less than for most of the1950 cohort cities. Being the nation’s capital, the location of substantial federal government employment and procurement spending, seems to have provided DC with an important measure of stability. Once again passing the 694,000 milestone in 2017—this time going up—shows that the city is still able to attract jobs and people in today’s continuously evolving economy.

The newcomer cities. As noted above, only four of 1950’s top cities are still among the dozen largest in the US. The departing 8 cities, all from the East and Midwest have been replaced by four from Texas, two from California, and one each from Arizona and Florida. Taken together, the newcomer cities increased by 8.7 million, a 375% gain. The largest gain, however, was Los Angeles, not a newcomer, which added 2.0 million and doubled in size. The biggest percentage gain was Phoenix at 1,412%.

In 2016 the top dozen cities accounted for 8.6% of the US population, a share close to half as much as the share (15.4%) the top 12 cities had in 1950. Of the increase in US population from 1950 to 2016, just 5.8% occurred in the nation’s top dozen cities. Traditional big-city boundaries seem thus to have diminished somewhat in relative importance even as the nation has continued to urbanize.

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About the data.

This is the second of three blogs about DC population based on the US Census Bureau estimate of DC population in 2017 released in December 2017.

The population data for the District of Columbia, other US cities, and the US is from the US Bureau of the Census. The data is for incorporated cities and not for the metropolitan areas of which they are a part. Annual data back to 1939 is accessed from Moody’s Analytics. Data for city rankings is accessed from the website https://www.biggestuscities.com. In the city ranking tables, 2016 data for DC do not reflect the revision made in December to that year by the Bureau of the Census.

A version of this blog first appeared in the January 2018 District of Columbia Economic and Revenue Trend report issued by the District of Columbia Office of Revenue Analysis, a component of the District of Columbia Office of the Chief Financial Officer.

This blog was revised on March 22 to clarify that the graph on p. 1 was population only.









DC’s population rose to 693,972 in 2017—9,636 above 2016. This was the slowest growth in 9 years.

However, growth in 2017 is 12,082 above last year’s estimate for 2016, as revisions added 3,166 people to prior years

In December 2017 the US Bureau of the Census estimated the population of states and the District of Columbia as of July 1st for that year and it also revised estimates for prior years. In the new estimate the Census Bureau added over 3,000 people to DC for the years 2012 through 2016, bringing 2016’s total to 684,336 (compared to the 681,170 it had estimated in December 2016). For 2017 the Census Bureau estimated DC’s population at 693,972 , an increase of 9,636 (1.4%) over the revised estimate for 2016.

The new data show that DC’s population continues to grow. Population has now increased for 12 straight years, adding 126,836 (22.4%) from 2005 to 2017.

The new estimates also indicate that DC’s population growth slowed in 2017. The 9,636 gain in 2017 was the slowest in 9 years, and less than the annual average of 10,570 that has occurred since 2005.

The components of DC’s population growth since 2000 show that slower growth in 2017 is mostly attributable to slower net domestic migration into DC. However, as noted below, the revisions to the years 2012 to 2016 create some uncertainty about the current trajectory of DC’s population growth, and they underscore the importance of migration in DC’s future population changes.

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table 2table 1a

Components of change. The Census Bureau breaks down population changes into two main categories—natural increase (the difference between births and deaths) and net migration (the difference between those moving in versus those moving out). In 2017 natural increase of 4,293 accounted for 44.6% of the growth in DC’s population from the prior year, and net migration added 5,312 (55.1% of all growth). The 2017 gains in both natural increase and net migration were below their respective annual averages in the 6 1/4 years from the April 2010 Census count to July 1, 2016, but the change in net migration explains most of the slower growth. Last year’s natural increase was 310 (6.7%) below the average from April 2010 to July 2016, while net migration was 3,104 (36.9%) less than the average of the prior 6 1/4 years.    graph 2

table 3b

Natural increase. Natural increase is positive because there are more births than deaths. From 2016 to 2017, births and deaths were both higher than the average over the prior six years. However deaths increased more than births (634 v 323), hence the slow down in natural increase.

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table 3a

Net migration. Net migration has two components: international and domestic, each of which is the net change of people coming to DC and those leaving. Over the past year the increase in net international migration was 486 (13.2%) greater than the 6 1/4 year average, while the increase in net domestic migration was far below that average. Net domestic migration of 1,152 from 2016 to 2017 was 3,590 (76%) below the average of the prior 6 1/4 years and is equivalent to all of the difference between slower growth in DC’s population in 2017 compared to the annual average from 2010 to 2017. Census provides only a net number for immigration, so the data does not indicate whether domestic migration slowed primarily because fewer people moved in or more moved out.

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How certain is it that DC’s population growth in 2017 slowed as much as the Census Bureau has estimated? The Census Bureau’s initial estimate for DC’s population in 2016 was quite similar to the current one for 2017—slower growth due principally to a sharp decrease in net domestic migration. The revisions to the 2016 estimates, including growth added to earlier years, were primarily due to increasing the net domestic migration estimates, and these revisions increased the 2016 population estimate from 681,170 to 684,336. As noted in the table, for the 6 1/4 years from April 2010 to July 2016 Census added 2,959 due to increased domestic migration, cut 829 from international migration, and added 350 for natural increase. This revision to 2016 suggests that 2017’s population estimate might also be revised in subsequent years as more information becomes available to Census from analysis of tax returns and other sources.

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The revision to 2016 and earlier years underscores the importance of net migration for the dynamics of population change in DC. Looking ahead, DC’s natural increase is not likely to change a great deal from year to year, ranging somewhere around 4,500 per year and unlikely to exceed 5,000 for some time. If DC is to continue to grow at a pace of 10,570 per year (the yearly average of the past 12 years) this means the city must experience a net gain in migration of around 6,000 per year. There is, of course, no way to know what migration will be in the future. The attractiveness of DC as a place to live will be balanced against factors such as housing prices, job availability, the quality of schools, and national immigration policies.

About the data.

This is the first of three related blogs concerning District of Columbia population based on the December 2017 US Bureau of the Census estimate of population in the city.

The population data is from the US Bureau of the Census which estimates population for all states and the District of Columbia in December as of July 1 of that year. Census also breaks down changes in total population into the categories of natural increase (excess of births over deaths) and net migration (the net of persons moving in and persons moving out, calculated separately for international and domestic migration). These components of change are shown relative to the past year and relative to the April 2010 census (a 7 1/4 year span). In this analysis, the components of change from 2016 to 2017 are subtracted from the total change from April 2010 to give a 6 1/4 year change from April 2010 to July 2016.

A version of this blog appeared in the December 2017 District of Columbia Economic and Revenue Trends which is issued by the District of Columbia Office of Revenue Analysis, a component of the District of Columbia Office of the Chief Financial Officer.


Over the past year, education and food services have become the largest sources of new jobs in DC

Those sectors’ contributions to wage growth in the District’s economy are, however, quite modest

According to US Bureau of Labor Statistics data, over the course of the last year job growth in DC came increasingly to be dominated by two sectors: education and food services. These two sectors were responsible for 11,800— about 90%—of the 13,133 net increase in all wage and salary employment in DC from September 2016 to September 2017. This high proportion of the job increase over the last year is remarkable because in September 2016 education and food services accounted for just 14% of all jobs in the city.

Graph 1

Rapid job growth in education and food services did not, however, translate into comparable income gains. The latest income data from the US Bureau of Economic Analysis (combined with seasonally adjusted employment data) indicates that the two sectors accounted for just 5.9% of the net gain in wage and salary income in DC from 2016.2 to 2017.2—despite job growth that exceeded the net increase for the economy as a whole.

table 1

Employment in the education and food services sectors. The increase in employment over the past year in both education and food services has been striking. From September 2015 to September 2017 education employment in DC went from 58,433 to 67,333. Of the 8,900 increase over these two years, 76% (6,800) occurred in the past year. Similarly, the last year accounted for 86% (5,000) of the 5,800 increase in food services employment over the past two years. As a share of all employment gains in DC, the combination of education and food services rose from about one-third or less in the year from September 2015 to September 2016 to over 100% during the spring of 2017.

Table 2

Graph 2

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Jobs and wages. The rapid increase in employment in education and food services over the past year appears to have made little impact on amounts earned by people working in DC. Whereas the two sectors accounted for 107.1% of the net increase in employment in DC from 2016.2 to 2017.2, their share of wage and salary growth was just 5.9%. This share of the increase over the year was less than the average share of wages attributable to those two sectors in 2016.2, which was 6.4%.

Part of the reason for the relatively small impact on wages that education and food services have had is that they both are relatively low wage sectors compared to DC average earnings. In 2017.2, the average annual earnings in education, $41,771, was just 45.7% of DC’s $91,405 average. The average wage in food services, $30,187, was one third of the DC average. Over the year 2016.2 to 2017.2, the average annual earnings fell in both education and food services.

Table 3

Table 4

What accounts for the rapid growth in employment accompanied by modest impact on wages in the education and food services sectors? The data on wages and employment do not provide a full answer to this question. However, two reasons probably help to explain why such a development might occur.

The first reason might be part time employment. BLS employer surveys count people who are working on the survey date, whether such people are full time or part time. Even if hourly wages are increasing, average annual earnings could result if the proportion of people working part time was increasing. Both education and food services can be a source of part time work. It is worth noting in this regard that the only other sector of DC’s economy in which average annual earnings fell from 2016.2 to 2017.2 was transportation and warehousing, another sector in which there could be an increasing amount of part time employment.

The second reason could be changes in the composition of employment within the sectors. This appears to be a factor in education where the largest gains in employment have been in the “all other” part of the sector. “All other” could be such things as charter schools or preschools or after school programs where average annual earnings are likely to be less than in colleges, universities, or professional schools.

Table 5

Graph 5Graph 4

It should be noted, however, that the data presented here is preliminary and the picture could change if it is revised in the future. The next significant revision to the employment data will occur in March 2018, and BEA’s income data is revised as more information becomes available.

About the data. The employment data in this analysis is from the US Bureau of Labor Statistics which conducts monthly surveys of employers in DC and throughout the US. The calculations are based on 3-month moving averages of seasonally unadjusted data. The September 2017 data reflects revisions which were included in the October 2017 employment report. Wage and salary data are from the US Bureau of Economic Analysis Personal Income series for DC, accessed through Moody’s Analytics. The most recent BEA data is for the 2017.2 quarter. For consistency in comparing employment and wage data, seasonally adjusted employment data was accessed from Moody’s Analytics. As noted, all BLS and BEA data is subject to revision.

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



The number of occupied apartment units in DC increased sharply last year

The increase tops the peaks of previous years

According to CoStar, a leading commercial real estate information firm, there were an estimated 174,917 occupied units in all classes of market rate apartment building in September 2017, an increase of 5,433 over the prior year. This was the largest one year gain since 2001, the period covered by the data base. The 5,433 one-year gain exceeded the annual gains that occurred before the Great Recession and following the relaxation of sequestration restraints.

graph 1

In 2005 DC’s population started to increase, with the city adding 125,000 residents by 2017, a 22% increase. Not surprisingly, this growth had an impact on the market for apartments. From 2005 to 2017.3 the net inventory of units increased by 35,615. This increase in supply was almost matched by growing demand as occupancy grew by 33,443. The overall vacancy rate rose only slightly—from 4.6% in 2005 to 5.1% in 2017.3.

The balancing by market forces of inventory and demand evident over the past decade is consistent with the modest decrease in the construction of new units that has occurred recently. In September there were 13,022 new units under construction, a decrease of 630 from a year earlier and of 1,687 from the peak pace of 14,709 in March 2017. While occupied units rose sharply over  the past year, inventory grew even more: a 6,727 net increase in inventory versus the 5,443 increase in occupied units. The vacancy rate in 2017.3 for all units also rose somewhat—to 5.1% from 4.7% a year earlier.

As noted in the accompanying chart, new construction began to accelerate in 2010. With some ups and downs, this was soon followed by annual increases in net inventory and in occupied units that have carried to the present time. Most of this activity involved Class A apartments. In the 7 years from 2010.3 to 2017.3, 88% of the net increase in apartment units and 84% of the increase in occupied units were accounted for by152 new Class A buildings. (Class A buildings are new or newly renovated, well located, generally larger buildings with higher rents.)   An increase of 69 Class B buildings accounted for about 15 % of the gains in inventory and occupancy. The number Class C buildings, representing about 38% of the District’s inventory of market rate units, declined over this time. Vacancy rates rose for Class A buildings, which require long lease-up periods, and fell for both Class B and Class C units.


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table 1

From 2009 to 2012, the three years in which DC experienced the largest annual increases in population over the past decade, the total increase in occupied apartment units for the three years was 4,987 (see the shaded area in the table below). This was less than the increased occupancy that occurred in the last 12 months. This suggests there have been some changes in the connections between a growing population and the city’s housing stock over the past 10 years or so. In the middle part of the decade of the 2000’s, more of the increase in population may have been accommodated by group homes or taking in roommates, by changes to single family or other smaller structures, or by owner-occupied units. Census Bureau estimates of DC’s population in 2017 will not be available until December, so comparison of population and housing unit changes over the past year is not yet possible. The ways in which the District and the owners of its housing stock adapt to changing demographics and housing patterns will no doubt continue to be an area of great interest.

table 2

table 3

About the data. The information is from CoStar’s historical data bases of market rate apartments which goes from the first quarter of 2000 to the third quarter of 2017.  The data includes the total for all apartment buildings as well as for buildings classified as Classes A, B, or C. CoStar Group, Inc. is an American commercial real estate information and marketing provider with headquarters in Washington, DC. Information in the CoStar data base is undated on a continuous basis.

The information here was included in the District of Columbia Economic and Revenue Trends report for October 2017, which was issued by the Office of Revenue Analysis of the Office of the Chief Financial Officer of the Government of the District of Columbia.


The Effect of Self-Employment on Personal Income Growth: Washington DC 2006-2014


The United States’ labor force has experienced great stresses and seen continuous change over the past decade. The recovery from the Great Recession and progressive technological change have combined to reshape how many workers participate in the labor market, the skills and attributes they possess, and their market compensation. As debates about the future of work in the United States swirl, a study by the Office of Revenue Analysis (ORA) looks at the characteristics of the self-employed and implications on their incomes after entering into self-employment.

The Self-Employed Population in DC

This study, which will be presented at the American Economic Association Annual Meeting in January 2018, looks at the self-employed population of DC, defined as people who actively work for money outside of the traditional employer-employee relationship. In order to identify the people who are actively self-employed on at least a part time basis, we use both Federal and DC individual income tax data to identify DC tax filers who meet at least one of these conditions below:

Fig 1_Corey

This identification process finds that the self-employed are about a fifth of all tax filers (between 45,000 and 56,000 tax filers in each of the years studied) in the District. They are spread relatively evenly across the eight wards of Washington, DC, are a bit younger than the overall tax-paying population, and are more likely to be married than the average DC tax filer.

The proportion of the population that is self-employed is also remarkably stable across time. The vast majority of the population is identified by one of the first three indicators shown in Figure 1, and fewer than a thousand in each year are identified solely because they take one of the smaller deductions. Roughly 75% are identified by more than one of the listed indicators.

Fig 2_Corey

A Look at Incomes

Compared to the overall distribution of DC tax filers, the self-employed subset is bi-modal. The figure below divides up the self-employed population into where they fall in the income percentiles for the entire District. There are 50 bars in this distribution, each representing two percentiles of income. All bars sum to 100 percent.
If the self-employed were evenly spread across the population, every bar would be at two percent, but we can see that the self-employed converge around the 11th and 99th percentiles in District income.

Fig 3_Corey

The self-employed population of DC has a higher mean income than DC as a whole. This helps it account for a larger percentage of the District’s total income than its percentage of the population as shown in Figure 2. At the lower income percentiles (i.e. bottom 25% and lower), the self-employed are generally less well off than the same percentiles for the rest of the population. The self-employed individuals at higher income percentiles (i.e. top 25% and higher) are much wealthier than the rest of the population at those same higher percentiles. Median incomes, however, between self-employed and non-self-employed residents are much more similar.  The impact of the recession is also more evident among the self-employed than among the rest of the population. Mean self-employed incomes fell each year from 2007 to 2010 and median income fell from 2008-2011, while the recession is not evident at all in the trend lines for the rest of the District’s taxpayers’ earnings. This is shown in Figure 4 below.

Fig 4_Corey

Some Determinants of Self-Employment

Using linear and logistic regression models (modeling approach to explain the relationship between a dependent variable and one or more explanatory variables), we examined four different time periods: 2006-2008; 2008-2010; 2010-2012; and 2012-2014 to identify possible determinants of self-employment and how they may have changed through the Great Recession and recovery. During the first year of each time period (i.e. 2006), the tax filer was not self-employed. In the second year of the time period (i.e. 2007) the tax filer met the criteria shown in Table 1 and was considered self-employed. The results compare the tax filer in the final year of the time period (i.e. 2008) to the first year in the period (i.e. 2006), prior to when the filer became self-employed.

We find that on average a wage drop in the first year of each time period is correlated with a roughly 5% increase in likelihood of becoming self-employed in the following year. This effect is greatest in the most financially troubled years from 2008-2012. During that time, the odds of becoming self-employed were twice as great among those who experienced a large wage and salary drop compared to those who did not. Interestingly, taking the mortgage interest deduction is as strongly correlated with becoming self-employed as experiencing a wage and salary drop.

Younger age is also correlated with becoming self-employed. The regression model implies that for every additional ten years of age, an individual is about 1% less likely to become self-employed in a given year.

Surprisingly, a filer’s level of total income had very little effect on their likelihood of becoming self-employed during our study periods. This indicates that self-employment is a phenomena that both high and low income individuals self-select into. This again speaks to the bi-modal distribution of the self-employed population as shown in Figure 3.

What Happens to Income After Becoming Self Employed?

Income gains do occur for those who become self-employed, but it seems that the gains are concentrated amongst those tax filers who were already in the top end of the income distribution prior to becoming self-employed. The rest of the self-employed population experienced an average decline in income after becoming self-employed in all study periods.

Those who became self-employed before the financial crisis and in the 2012-2014 period afterward experienced an increase in income. However, these income gains are confined to the top 10 percent of income earners in all periods. The gains that upper decile earners can expect from becoming self-employed are quite large, ranging from $14,000 to $50,000 depending on the time period. Conversely, becoming self-employed if you are in the lower 75% of the income distribution, led to short term losses ranging from $1,700 to $3,500 in the third year of each time period studied.

Some Takeaways on Self-Employment

Self-employment is a common phenomenon in the District, as over 20% of all tax filers in a given year can be classified as self-employed (on at least a part-time basis). We see that from 2006-2014, the number of self-employed residents has grown in line with the overall population growth in the city. We find that the self-employed subset of the population has a bi-modal income distribution and that the average newly self-employed individual is likely to experience net income decreases unless they are in the higher end of the income distribution prior to taking on self-employment.

One possible factor that explains this phenomenon of the income losses of those who switch to self-employment is that income gains may not be their ultimate goal. Being self-employed for many people may be a lifestyle decision that they make for greater flexibility, the pursuit of entrepreneurial endeavors reasons, or family obligations, among others, and not solely to earn more income. Indeed, a growing number of DC residents appear to be substituting slightly higher incomes to experience more work and lifestyle freedoms.

What exactly is this data?

I used administrative individual income tax records from the IRS and the DC Office of Revenue Analysis to identify self-employed DC residents in the years 2006-2014. Due to data limitations, however, our identification methodology cannot distinguish between an independent contractor, a small business owner or an occasional gig worker. The people we identify as self-employed are likely getting into vastly different lines of work and likely for vastly different reasons.

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.