DC’s $15 Minimum Wage: The Commuter Effect

Last week, we presented an overview of the effects of DC’s $15 minimum wage (full paper). Part two of our analysis focuses on “The Commuter Effect”. DC is surrounded by higher population jurisdictions that have increasingly lower minimum wages when compared to DC. This incentivizes more nearby Virginia and Maryland residents to compete for employment in DC. The result of this competition will force some DC residents who previously would have been able to find jobs in DC to have to look elsewhere.

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VA: Fairfax County, Arlington County, Alexandria   |   MD: Prince George’s County, Montgomery County

As the above chart shows, DC residents make up half of those working in DC and earning $12.50/hour or less. They make up a much smaller percent of those working outside DC. As DC’s minimum wage continues to increase to $15/hour, the group working outside DC will have greater and greater incentive to find work in DC. This will change the proportion of those “Working in DC” to look more like the group that is currently “Working Outside DC,” and that means proportionally fewer DC residents.

Job Losses

The commuter effect is the main reason that DC residents will lose 82% of all jobs lost in DC due to the minimum wage increase. Our model predicts by the year 2026, 2,489 total jobs will be lost, with 2,046 of those jobs previously being held by DC residents. Without the commuter effect, our model still estimates that there would be job losses as businesses and consumers react to changes in prices due to the minimum wage increase, but the commuter effect concentrates the losses on DC residents.

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What exactly is this data?

American Community Survey data was used to show where people live and work in the DC area. For a similar take on this data, see our previous post on DC workers and where they come from.

DC’s $15 Minimum Wage: What will its impact be?

Back in June 2016, the DC Council and Mayor approved the Fair Shot Minimum Wage Amendment Act of 2016. This bill stipulates that the DC minimum wage (currently at $11.50) will increase to $15.00 an hour by 2020. A recently completed study analyzes the potential effects of the higher minimum wage on DC.

Based on this legislation, the minimum wage will increase per the timeline below. (Note: we estimate an annual 2.3% inflation adjustment for the previous minimum wage policy beyond 2016.)

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Who is affected by the higher minimum wage?

We estimate that of the roughly 750,000 total workers in DC proper (excluding self-employed and proprietors), 150,000 will be impacted by the higher minimum wage. For DC residents who both live and work in DC (about 345,000 people), we predict that about 61,000 will be impacted by the new policy.

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How are DC residents affected?

Most of the impacted District residents (those earning between $8.25 and $18) will see an increase in their wages over the baseline of up to $5,100 in 2021 (one year after the policy hits the $15 per hour mark). About 1,200, or 2% of the 61,000 residents, however, may face job loss by 2021. This number increases to (and caps out) at around 2,000, or 3.4%, by 2026.

For all DC residents impacted by the minimum wage policy (including those who lose their job), net total earnings in the city increase by about $140 million in 2021.  There’s about $190 million generated in new earnings by the higher wage, but $40 million is offset by those who lose their jobs and another $12 million is lost by those earning above the minimum wage who see a slight slowdown in subsequent wage growth as employers try to shift some of the new, higher labor costs.

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Main takeaways

This policy, which first impacts DC on July 1st, 2017 (when the minimum wage rises to $12.50), affects nearly 20% of all workers in DC. While 2-3% of DC resident workers may experience job loss, the remaining residents are expected to see wage gains of up to $5,100 by 2021.

What’s interesting is that almost 2/3 of the increased earnings produced by this policy in 2021 go to non-DC residents who work in the District. Yet, over 80% of the job losses are absorbed by DC residents by 2026. This is due to the ‘commuter effect’ which we’ll talk about in our next blog.

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A secondary finding of the study is that over 60% of the 61,000 affected DC residents are EITC recipients and nearly all of them will see a reduction in their EITC benefit. However, their higher wages will leave them better off on net. We also looked at the effects on businesses (i.e. costs, competitiveness, etc.) across some of the most impacted industries in DC. Both the EITC and business effects will also be discussed further in upcoming blogs.

What exactly is this data?

We used data from the BLS (Occupational Employment Statistics 2014), Census (American Community Survey), and DC income taxes to model what the effects of this bill may be. From the OES data we identify, in each of the 800 occupations they detail, the number of workers in the District who are likely subject to the minimum wage and those that would benefit. Using ACS data, we’re able to estimate the number of DC residents who are affected.

We define “affected” as workers who earn between $8.25/hour (the minimum wage in 2014, the year the BLS data is from) and $18/hour. We use $18/hour as the upper bound to account for the “spillover effect”, where workers who earn just above the new minimum wage of $15 also see an increase in wages. For example, if a shift supervisor at a restaurant was earning $16/hour while a hostess was making $12/hour, when the minimum wage raises the hostess’s earnings to $15/hour, it is likely that the supervisor would also see a wage increase in order to prevent just a $1/hour difference in wages between the two positions ($16 v. $15). The supervisor may not get the same $3 increase in wages that the hostess received, but there would still likely be some increase in the supervisor’s wage.

Impacted Groups: All DC Workers DC Residents
Those earning $15 and below (directly affected) ~115,000 ~47,000
Those earning $15-18 (spillover) ~35,000 ~14,000
Total People Affected ~150,000 ~61,000

D.C.’s Immigrant Workforce

Around 829,000 people work in D.C. (within the city-proper), and about 26 percent of them are immigrants. Today, the Washington Post reports that some of D.C.’s immigrant workers, particularly those working in restaurants and some daycare centers and schools, are going on strike.

Indeed, the industries with people on strike have some of the highest concentrations of immigrants in D.C., as you can see in the graph below.  Seventy-one percent of chefs and head cooks working in D.C. (within the city-proper) are immigrants, as are 61 percent of lower-rank cooks. Fifty-seven percent of childcare workers in D.C. are immigrants. The occupation in D.C. with the largest concentration of immigrants is carpenters, 80 percent of whom are immigrants. (In our analysis we only looked at occupations with more than 3,000 workers in D.C.)

Most of the occupations with the highest concentrations of immigrants in D.C. are those with low or middle wages. However, immigrants comprise almost half of D.C. workers in several high-wage occupations: economists (46 percent), mathematicians and statisticians (43 percent), and physical scientists (42 percent).

We define a job as low-wage if its median wage was in the bottom 25 percent of median wages across all jobs in D.C (or below $44,000). High-wage jobs have median wages in the top 25 percent (or above $86,000) and middle-wage jobs are in between.

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While low-wage jobs in D.C. have the highest concentration of immigrants (41 percent of all low-wage workers in D.C. are immigrants, compared to 22% of middle- and high-wage workers), the number of immigrants in low-wage jobs in the city is roughly equal to the number of immigrants in high-wage jobs, since the city has many more high-wage workers. There are about 75,000 immigrants in low-wage jobs in D.C. and about 73,000 immigrants in high-wage jobs.

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What exactly is this data?

Our data on immigrants by occupation comes from the 2015 American Community Survey 1-year PUMS data. “Immigrants” include naturalized U.S. citizens and non-citizens. “D.C. workers” are people who live in D.C., Maryland, and Virginia who report D.C. as their place of work. We only look at occupations with more than 3,000 people working in D.C. in order to reduce sampling errors. Because the ACS is based on a sample, there is a margin of error in all of our calculations. Our calculations should be treated as estimates, not precise counts.

Our wage data comes from the May 2015 Bureau of Labor Statistics Occupational Employment Statistics Survey. We define a job as low-wage if its median wage was in the bottom 25 percent of median wages across all jobs in D.C (or below $44,000). High-wage jobs have median wages in the top 25 percent (or above $86,000) and middle-wage jobs are in between. In cases where the occupation code in the ACS data did not match the occupation code in the BLS data, we calculated a median wage using the ACS data.

 

What is that extra bathroom worth?

The scenario is all too familiar- searching the web listings you come across the seemingly perfect 2- bedroom row house –it’s in the right location, close to public transit and good schools, not next to the fire station, it’s more than 12 feet wide so the stairs don’t eat up all the space, it has a decent size garden and a finished basement, it’s under $1 million.  Then it all comes crashing down -it only has one bathroom!

Even the prospective single buyer knows that while one bathroom might be perfectly suitable for her/his living arrangements, on resale this is likely to affect value and narrow the field of prospective buyers.

In this post we crunched the numbers to see how much an extra bathroom is worth and the difference with respect to a half-bathroom.

Here’s what we found based on an analysis of market values for over 15,000 2-bedroom single family homes.

Average value of a an additional full and a half bathroom in a 2 bedroom home in DC  

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Difference in value compared to a 2 bedroom- 2 bath

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On average, the extra full bathroom results in a difference in value of about 15 percent, or almost $60,000.

The average figure masks significant differences among neighborhoods.  In Georgetown, the differential between a one bathroom and 2 bathroom property was almost 25 percent.  Some of the differential is due to the fact that a 2 bedroom 2 bath house is simply larger- larger bedrooms, a larger living room- so that not all of the incremental value is due to the extra bathroom. We estimate that about 1/3 of the additional value or about $75,000 is due to the extra bathroom. (See graph below for a comparison of select neighborhoods)

In less expensive neighborhoods, like Trinidad and Brookland, the difference was less pronounced, only about 15 and 11 percent respectively.

Having 1.5 baths narrows the differential in value with respect to a full 2 bathroom considerably. The average difference in value compared to a full 2 bathroom is $23,000, or about 6 percent.

Comparison of values of 2 bedroom homes by number of bathrooms and neighborhood 

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The upshot

The large premium that a 2 bedroom 2 bath row house commands over a house with only a single bathroom  makes it worthwhile to ask an architect friend if she/he can squeeze in at least a half bath. Perhaps they can combine the hallway closet with the kitchen pantry. This is certainly the case in neighborhoods like Georgetown where the increase in value from having the extra full or half bathroom would compensate for the cost.

What exactly is the data?

Data on property values were obtained from the Office of Tax and Revenue and refer to values in 2015. As noted above these are average differences.  Values will vary considerably based on others factors such as the condition of the home, square footage, the presence of other amenities like fireplaces, garages, etc.

Bob Zuraski contributed to this post

 

D.C.’s Cashiers and Janitors Are More Likely to Live in the City than Other Workers, but That’s Changing

Nearly 800,000 people work in the District of Columbia, yet only about 30 percent of the District’s workers live in the city-proper. Workers in low-wage jobs are more likely to live in the city than those in middle- and high-wage jobs. Thirty-nine percent of D.C.’s workers in low-wage jobs lived in the city between 2010 and 2014, compared to 30 percent in middle-wage jobs and 27 percent in high-wage jobs.

We define a job as low-wage if its median wage was in the bottom 25 percent of median wages across all jobs in D.C (or below $44,000). High-wage jobs have median wages in the top 25 percent (or above $86,000) and middle-wage jobs are in between.

You can see how this plays out by occupation in the graph below. Cashiers, janitors, childcare workers and others in low-wage jobs are more likely to live in the city than most other workers, though people in a handful of middle- and high-wage occupations, like managers of social and community services, teachers, and chief executives, have relatively high rates of living in the city too. Registered nurses and police officers (which include transit and federal police) are the least likely to live in the city.

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Here’s where these workers live:

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People in low-wage jobs tend to live in the city more than others, but that’s been changing over the past decade, as you can see in the chart below. The city is losing construction workers, cashiers, childcare workers, and janitors, and gaining people in high-wage jobs, like managers of social and community services, operations research and management analysts, and economists.

In less than a decade, the workers most likely to live in the city shifted from cashiers, retail salespersons and clerks (50 percent lived in the city in 2005-2009) to managers of social and community services (47 percent lived in 2010-2014).

Meanwhile, over the same time period, the least likely to live in the city switched from software developers (9 percent in 2005-2009) to police officers (11 percent in 2010-2104).

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As the graph below shows, this is part of a larger pattern of D.C. workers in middle- and high-wage jobs starting to show a preference for living in the city, and workers in low-wage jobs increasingly living in the suburbs – a trend that’s unsurprising given the District’s increasing cost of housing. The percent of workers in low-wage jobs living in the city decreased from 43 percent to 39 percent between 2005-2009 and 2010-2014, while the percent of workers in high-wage jobs living in the city increased from 24 percent to 27 percent over the same time period. These changes may seem small, but they are statistically significant at the 99 percent confidence level.

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What exactly is this data?

Wage data: Our wage data comes from the Bureau of Labor Statistics Occupational Employment Statistics survey of D.C. workers from May 2015. We define a job as low-wage if its annual median wage was in the bottom 25 percent of annual median wages across all jobs in D.C (or below $44,000). High-wage jobs have annual median wages in the top 25 percent (or above $86,000) and middle-wage jobs are in between.

Percent of Workers Living in the City: Our data comes from the American Community Survey PUMS data for 2005-2009 and 2010-2104. For our universe of D.C. workers we started with everyone living in D.C., Maryland, or Virginia who works in D.C., so we are excluding long-distance commuters who work in D.C. but live in places outside of D.C., Maryland, and Virginia. When we analyzed specific occupations, we looked at all occupations with 8,000 or more workers in D.C., with the exception of miscellaneous managers since the category is vague. We grouped some occupations together so they surpassed our 8,000 person threshold.

Map of Where Workers Live: This data comes from the American Community Survey PUMS data for 2014. We only look at workers who work in D.C. and live in either D.C., Maryland, and Virginia. All of the occupation groups in the map have 8,000 or more people working in the city.

Police Officers: Police officers in this case includes more than just people employed by the Metropolitan Police Department; it also includes transit police, federal police, and police who said they work for private organizations. In 2014, the Metropolitan Police Department released data showing 17 percent of its officers live in the District.

Errors: The data in this post have various margins of error since the data comes from surveys. In most cases we used a five-year data set to reduce the errors, and only looked at occupations with 8,000 people or more. The errors are highest for the map of where people live because for that we had to use a one-year dataset (geographic boundaries changed within the five-year dataset, making a map more difficult to produce). The map is intended to give readers a general sense of where people live; we discourage people from using it for direct area-to-area comparisons. Our findings on the loss and gain of workers of different occupations and wage levels are in many cases statistically significant and we have noted this in the post.

 

District injury death rates down since 1999

The incidence of death by injury—which includes accidental and intentional deaths—has been on the decline in the District. The rate of death by injury decreased by 12.7 percent between 1999 and 2014, despite a steady national increase in the rate over that time. The injuries counted include accidental deaths such as a falls or drug overdoses, as well as intentional deaths such as murder or suicide. The actual number of deaths by injury in 2014 (385) is about the same as in 1999 (382), but because the population in the District has steadily increased over that time, the rate has decreased. Only two other states – Alabama and Nevada – saw a decrease from 1999 to 2014, and the District had the largest percentage rate decrease of any state. While some year to year fluctuations occurred in all the states throughout the 15 years, most states have seen a steady rise in death by injury rates.image001.png

Until 2010, the District stood apart from Virginia and Maryland as the incidence of death by injury in the city was above the national level. Now the District is more like its neighboring states, all three of which are now below the national rate. image003.png

The decline in intentional injury deaths (murder and suicide) is the major factor in the overall decrease in the District’s injury death rates.  Compared to 1999, intentional injury death rates have decreased by 40% in the District, while accidental deaths rates have increased by about 17%, although both categories have been somewhat erratic over the period.

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Accounting for Population Size

It may be argued that comparison to the national rate is not useful, given the District’s small population. So we also looked at how the District compared to the four least-populous states, two with fewer people than the District (Vermont and Wyoming) and two with more people (Alaska and North Dakota).  We found the incidence of death by injury has increased in all four of these states.  Alaska’s injury rate grew 11%, Wyoming grew 15%, North Dakota grew 25%, and Vermont grew a whopping 53% increase over 1999 rates.

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While intentional injury death rates decreased significantly in the District, the other least populous states saw increases.image009.png

All of the least populous states saw increases in accidental injury death rates.image011.png

Could the growth the District has experienced over the last 15 years be playing a factor in the injury death rate decrease?  If so this would be unique to the District.  States with population growth rates higher than the District experienced an injury death rate growth of an average 13.7%, while states with population growth lower than the District experienced an average injury death growth rate of 28.8%.  But individual state rates vary so widely, this is not sufficient explanation. Population growth may play a part in our lower injury death rate, but nearly all states saw an increase, so the District’s 12% decrease still stands out.

Other Demographics

When we break out the total deaths by sex and race, some of the totals are too small to analyze on a year to year basis.  However, we can report that overall injury death rates for whites in the District have increased 43% since 1999, while rates for African Americans has decreased by 7%.  These results are driven mainly by the reduction in intentional injury death rates for African Americans, and an increase in accidental injury death rates for whites.

Injury death rates for females in the District have increased 62% since 1999, while rates for males have decreased 28%.  Boys and men are still more likely than girls and women to die of injury, which fits with earlier research (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3222499/), but the trend away from this stands out.

 

What is this data?

The data source is the Fatal Injury Data in the National Vital Statistics System (NVSS) operated by the National Center for Health Statistics under the Centers for Disease Control. (http://www.cdc.gov/injury/wisqars/fatal_injury_reports.html)

DC’s startup economy- How much does it pay to work at a startup in DC compared to other companies and other cities?

Start-up companies play a vital role in the economy, fostering innovation and providing job opportunities for those who want to go at it on their own and/or prefer to work in what is typically a less hierarchical environment.  In the digital information era, the glorified image of young entrepreneurs and workers who start hugely successful companies masks some of the risks associated with working at startup companies. Typically these companies do not have the deep pockets to pay salaries comparable to established companies and failure rates among startups tend to be higher than for established companies.  This may be a risk worth undertaking as the payoffs for working at start-up companies that eventually become successful can be significant, particularly in high tech companies that go public. For younger individuals, job security and pay related to seniority and tenure can be less of a factor than for older individuals, making the risk of working at a startup less severe.

In this post we examine how average salaries for startup companies compare to salaries across companies in DC and other cities. We use industry-wide data for all age groups and then show this separately for 25-34 year olds.

Table 1: Average Salaries for Startups vs All Companies, All Age Groups

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Source: US Census Bureau, DistrictMeasured.com

  • As shown above, for all age groups, salaries at startups ranged between 56 percent and 76 percent of salaries at all companies among the comparison cities.
  • DC was at the lower end of the range at 61.2 percent exceeding only NYC at 56.2 percent.

Table 2: Average Salaries for Startups vs All Companies, 25-34 year olds

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Source: US Census Bureau, DistrictMeasured.com

  • For the 25-34 year old age group the ratio of start-up salaries compared to all salaries was higher than the average for all age groups shown in Table 1. This is likely related to the fact that salaries for younger individuals are typically lower and more compressed to begin with.
  • The ratio of pay varied considerably among cities.
  • In San Francisco and Austin, the pay for young individuals working for startups was comparable to the pay at more established companies.
  • In San Francisco the pay for 25-34 year at startups exceeded pay for all other age groups.
  • DC and New York were at the lower end of the scale again. Pay at startups was, respectively, 72.7 percent and 68.5 percent of the pay at more established companies.

Here is a summary graph of the pay ratios for all ages and the 25-34 year old age group.

Graph 1: Ratio of Salaries at Startups Compared to all Companies, Ages 25-34 and All Ages   3

Source: US Census Bureau, DistrictMeasured.com

Finally we looked to see if there was considerable variation among select industries that could explain some of the differences in pay at startups in DC compared to San Francisco for instance.

Here’s what we found:

Table 3: Startup pay to ratio to all companies among industries in San Francisco and DC, Ages 25-34

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Source: US Census Bureau, DistrictMeasured.com

Notably, in San Francisco the pay ratio for Professional Scientific and Technical Services, one of the largest industries for tech startups, far exceeded that in DC, almost 100 percent compared to 78 percent.

With the exception of Health Care and Social Services, pay ratios in the other industries also exceeded or were similar to DC.

Summary

The difference in pay ratios for start-up pay likely reflects a more vibrant start up economy in San Francisco and Austin, compared to more traditional career paths in established financial and legal services firms in DC and NYC. The causes for this could include- stronger ties to venture capital funding that provide greater financing to startups , or simply stronger competition for young talent among startups in San Francisco and Austin.

What exactly is the data?     Data on wages is from the U.S. Census Bureau, Local Employment Dynamics Data for 2014. Start-up companies are defined as firms that are less than 4 years old.

Bob Zuraski contributed to this report