Cherry Blossom Season and The District’s Sales Tax

Peak blossoms for the cherry trees in Washington DC were expected earlier this year because of unusually warm weather for many days in February and early March. However, starting March 10, there were ten consecutive days of freezing weather with a wintry mix of precipitation on March 14, putting the official date of peak bloom in jeopardy. (The National Park Service defines peak blooms as the day when 70 percent of Yoshino cherry blossoms are open).

We wondered if the change in weather would damage the cherry blossoms, and consequently if this would have an impact on sales tax collections. Since it’s too early to tell the impact for 2017, we looked at data on cherry blossom peak dates over recent years, to see if there is a visible pattern on the corresponding sales tax collections.

Because the Cherry Blossom Festival spans both March and April, we looked at data for those months individually and combined for fiscal years between 2005 and 2016. We compared past sales tax collections with the historical peak bloom dates. We also analyzed past sales tax collection data to see if the strength of March and April activity ties to an overall better sales tax performance for the fiscal year.sharain1

As figure 1 shows, over the period FY 2005 to FY 2016, the month of the peak blossoms (March or April) was also the month of higher sales tax activity in ten out of twelve years. In 2008 and 2009 the peak month differed from the month of higher sales activity.

In fiscal years with strong collections in springtime (March and April), the total sales tax collections for the entire year were also strong.  In the years 2006 and 2009, where sales tax collections during the spring were not as strong as the previous year, total collections for the entire fiscal year also were not as strong as the previous fiscal year.

As figure 2 shows, the contribution of the cherry blossom season to the District’s sum of sales tax is clear, sales tax collections from these two months, for the period FY 2005 to FY 2016, average about 17.7 percent of total sales tax collections during the year.

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What exactly is this data?

Our data on sales tax is from the Office of Revenue Analysis monthly cash collections reports. Information on Peak Bloom dates were obtained from the National Park Service also available at https://www.nps.gov/subjects/cherryblossom/bloom-watch.htm

Seble Tibebu and Bob Zuraski contributed to this post.

Revised data show more jobs located in DC in 2016, a slower pace of growth at year end, and a different view of recent trends

As it does each year at this time the US Bureau of Labor Statistics (BLS) revised its labor market data for all of the states and the District of Columbia based on additional information that has become available. For DC, this year’s revisions showed that at the end of last year—the December 2016 quarter—there were 2,267 (0.3%) more wage and salary jobs located in DC, but 4,052 (1.1%) fewer employed DC residents than had been previously estimated. (See below for BLS’s explanation of the basis for the revisions.)

 

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These revisions to DC’s final quarter of 2016 seem relatively modest, but there is more to the story. The revisions over the past two years changed the pattern of growth not only for DC but for the Washington metropolitan area as well. These revisions result in a changed picture about how the recent dynamics of DC’s labor market compare to those in the metropolitan area and the US. We look at five such changes.

1. DC job growth at the end of the year was slowing down, not speeding up. The revisions increased job growth over the last half of 2015 and the first part of 2016, but reduced it in the last half of 2016. In the 2015.4 quarter , for example, job growth over the prior year was revised upward from 8,700 to 18,633—more than double. Even though 2,267 jobs were added to the 2016.4 quarter, the year ended with job growth slowing rather than speeding up.

graph 1

2.At the end of the year DC’s rate of job growth was below the US average, not above it. Previously, it appeared that an increasing rate of growth in jobs brought DC to the point where its rate of increase in jobs exceed the national rate of 1.6% in the 2016.4 quarter. The revision boosted DC’s rate of growth above the US for most of 2015 and the first half of 2016, but it slowed DC’s rate to well below the US average by the end of the year.

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3. At the end of the year DC private sector jobs were growing at a faster rate than public sector ones, not at a slower rate. The upward revision of 2,267 jobs for the 2016.4 quarter was a net number, resulting from a 4,867 cut in the public sector and an increase of 7,133 in the private sector. The decrease in the public sector was mostly in federal government jobs (down 3,733), but local government ones were also reduced by 1,133. In the private sector there was modest increase in professional and business services (367), but most (6,767) was a 1.8% net increase in all other parts of the private sector.

The revision was enough to change the relationship of DC’s public and private sector job growth over the past two years. Previously, the rate of increase in public sector employment was shown overtaking the private sector in 2016. The revision substantially cut the growth of public sector jobs in 2016, so that they grew more slowly than private sector ones—even though growth in the private sector was slowing.

 

table 2

 

 

graph 4agraph 5

 

4. DC’s rate of private sector growth over the past two years has been similar to that in the suburbs, not significantly different.   The revisions to Washington metropolitan area wage and salary employment cut 19,033 jobs from the area total in the 2016.4 quarter, a 0.6% reduction. The net reduction in the metro area total was entirely due to a 21,300 (0.9%) reduction in suburban jobs. Most of the suburban reduction, 16,167, was in the private sector—7,133 of which was shifted to DC and 9,033 was lost to the area. The suburban private sector loss was about equally divided between business and professional services and all other private sector jobs.

A consequence of the change to metropolitan area job growth over the past two years is that the pattern of change in DC’s private sector is now seen to track that of the suburbs fairly closely. Previously, the rate of change in DC’s private sector appeared to be much weaker than in the suburbs over most of the past two years. With the revision, DC’s private sector is now shown to have grown faster over most of that time, just falling below the suburbs at the end of 2016.

 

table 3.PNGtable 4.PNG

 

graph 6graph 7

5. DC resident employment did not end the year with a sharp increase. The 4,052 (1.1%) downward revision to resident employment in the 2016.4 quarter was notable because it reversed a sharp increase which previously had been reported. This revision mostly results from cuts to the labor force (a 1.2% cut of 4,832), not higher unemployment. (Unemployment was actually reduced by 779, resulting in a 0.1 percentage point reduction in the unemployment rate.) The reduction to the labor force is consistent with slowing population growth which occurred in 2016. DC ended the year with growth rates in the labor force and resident employment similar to those of the Washington area suburbs and the US average.

 

table 6graph 8

 

 

graph 9.PNGgraph 10.PNG

 

According to BLS, momentum in DC’s labor market seems now to be slowing at a time when federal spending policies under consideration may weaken the region’s economy. Should such policies materialize, the preceding discussion underscores the difficulty of keeping current with how well DC’s labor market is responding to the new environment. Data can be revised.

What is this data?  All  data is from the US Bureau of Labor Statistics (BLS).  One set  is wage and salary employment (which is determined from surveys of employers) and the other set is labor force and unemployment statistics (determined from surveys of population). Both sets of data are for the District of Columbia, the Washington DC  metropolitan area, and the total US economy, and cover the period from the fourth quarter of 2014 (2014.4) to the fourth quarter of 2016 (2016.4). The data referred to in the text as the “previous estimate” is the data issued in January 2017 for the period up to an including December 2016.  The data referred to in the text as the “revised estimate” was issued in March (February in the case of the US data) for the period up to an including January 2017.

The BLS web site explains the basis for the labor market data revisions as follows:

Nonfarm payroll estimates for states and metropolitan areas have been revised as a result of annual benchmark processing to reflect 2016 employment counts primarily from the BLS Quarterly Census of Employment and Wages (QCEW), as well as updated seasonal adjustment factors. Not seasonally adjusted data beginning with April 2015 and seasonally adjusted data beginning with January 2012 were subject to revision.

The civilian labor force and unemployment data for states, the District of Columbia, and modeled sub-state areas were revised to incorporate updated inputs, new population controls, re-estimation of models, and adjustment to new census division and national control totals. Both not seasonally adjusted and seasonally adjusted data were subject to revision from January 2012 forward.

Data for the DC suburbs is calculated by subtracting District of Columbia estimates from those for the entire Washington metropolitan area.

The information here was presented in the District of Columbia Economic and Revenue Trends: March 2017 prepared by the DC Office of Revenue Analysis.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

What Drives District Retail? Household or Business Purchases?

Sales tax returns data show that the District’s retail sector has grown in importance since 2001. This may not be accidental. Attracting retail to the District has been a focus of District policymakers in recent years. But the data also shows that since the Great Recession, which lasted from December 2007 to June 2009, growth in the sector has slowed. As such, tracking the trends in the sector and understanding the forces driving these trends could better inform policymaking.

In a newly issued report, we use sales tax returns for fiscal years 2001 to 2014 to investigate annual trends in retail sales tax receipts over the period. The analysis categorizes the top 50 retail sales tax returns by revenue each fiscal year into two broad categories of purchases: household and business.  We focus on the top 50 retail sales tax filers because the coding for industry sector on the raw returns data is unreliable and re-coding the entire database of tax returns would be tremendously time-consuming.

Figure 1 shows that in FY 2014 retail sales taxes paid by the top 50 tax filers was about $130 million compared to about $470 million in total retail sales tax filers. That is, the top 50 filers paid about 28 percent of all the retail sales tax receipts. This share did not change by much over the period covered by the analysis. In FY 2001, the top 50 retail sales tax filers paid about $110 million compared to $330 million for all retail sales tax filers, or about a third of all retail tax receipts.

crankFigure 1 also shows that, for the period FY 2001 – 2014, the pattern of growth of retail sales tax receipts from the top 50 filers was roughly similar to the pattern of growth of total retail sales tax receipts: retail sales tax receipts of the top 50 filers grew when overall retail sales tax receipts grew and slowed when growth of overall retail sales tax receipts slowed. However, growth rates of retails sales tax receipts for the top 50 was different from growth rates of overall retail sales tax receipts. Table 1 shows that prior to the Great Recession annual average growth of retail sales tax receipts from the top 50 taxpayers was 3.7 percent compared to annual average growth of 5.3 percent for the total retail sales tax receipts. The period during and after the recession (FY 2007-2014) the annual average growth of receipts from the top 50 taxpayers grew about 2 percent, while receipts from all other taxpayers was slightly negative. For the entire period the annual average growth of retail sales tax receipts from the top 50 taxpayers was 1.3 percent compared to annual average growth of 3.3 percent for total retail sales tax receipts.

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Trends in Household vs. Business Purchases

For a closer examination of the underlying trends in the District of Columbia’s taxable retail sector, the city’s top 50 retail sales filers are classified by their respective North American Industry Classification System (NAICS) industry grouping. These industries are further grouped into two categories: Household or Business. Sales tax returns from sectors more likely to sell to households for final consumption are classified as Household, while sales tax returns from sectors more likely to sell to businesses for final consumption or as inputs to the production of other goods and services are classified as Business.

Number of Sales Tax Filers

Figure 2 shows that, of the top 50 sales tax filers, those selling primarily to businesses outnumbered those selling primarily to households for 10 of the 14 years of the study.   In FY 2001, 26 out of the top 50 sales tax filers sold primarily to businesses; this rose to 28 in 2002. For years 2003 to 2006 there was more or less an even number of sales tax filers selling primarily to businesses as those selling primarily to households. From FY 2007 to 2014 sales tax filers selling primarily to businesses again outnumbered those selling primarily to households, with only 20 of the top 50 filers selling primarily to households in FY 2014. Figure 2 also shows that, since the recession, the margin by which the number of filers selling primarily to businesses exceeds the number selling primarily to households has increased. This is somewhat surprising as the story of the District since the recession is the increase in population, with the new residents being younger and relatively richer. Given the population growth one would expect relatively faster growth in the number of filers selling primarily to households. Online shopping may be the missing factor here. Until this fiscal year, e-commerce filers were excluded from the sales tax base, and the new residents are in the demographic of those more likely to be online shoppers.

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Retail Sales Tax Receipts

The distribution of retail sales tax filers between household and business purchases yields useful insights, but ultimately we are interested in the relative amount of retail spending by the two groups. So let us turn to the relative spending by households and businesses as measured by retail sales tax receipts. Figures 3 and 4 show that between FY 2001 and 2014, except for fiscal years 2003 through 2006, when retail sales tax receipts from business and household purchases were more or less even, among the top 50 sales tax filers business purchases accounted for more of the receipts than household purchases, both in levels and as a share of the total. Figure 3 also shows that the business purchases component of the retail sales tax is more stable than the household purchases component.

While the household purchases component fell steeply in FY 2007 at the onset of the Great Recession, the business purchases component actually rose slightly. The household component rebounded in FY 2008, but fell back the following year to its FY 2007 level and has since remained below its pre-recession peak. The business purchases component also fell in FY 2009, but not as steeply as the household component. Since the recession the business purchases component has been more or less stable, except for FY 2011, when both it and the household component fell. This followed the passage of the federal Budget Control Act of 2011, which mandated federal budget cuts to reduce the deficit by $1.2 trillion over ten years. The business purchases component recovered in FY 2012 to about the level it was in FY 2010 levels and has remained relatively flat since. While the household component has not returned to pre-recession levels it has grown in the last 3 years.

So what’s driving the trends in the relative growth of the household and business purchases components of the retail sales tax? One place to look is federal spending. As the largest single employer in the District, the federal government plays a large role in the District’s economy. Although federal government purchases are not taxable, federal spending flows through to households and businesses whose purchases are taxable, so shifts in federal spending may lead to shifts in the household/business composition of District sales tax receipts.

The line graph in Figure 3 shows the level of federal nondefense spending over the FY 2011 – 2014 period. It shows that prior to the recession, both the household and business components of sales tax receipts grew along with nondefense federal spending. After the recession, changes the business component, which was never hit hard by the recession, continued to mirror, more or less, changes in federal nondefense spending, including the flattening after the implementation of the Budget Control Act of 2011 that curbed the growth in federal spending. The household component, which was hit harder by the recession, seems no longer tightly linked to changes in federal spending. One reason for this change may be that, post-recession, District residents, who in recent years have become younger and hipper, are bargain shopping on-line to a greater degree at the same time that the choices for on-line shopping have been expanding. But data to confirm this is scarce. If, in fact, greater on-line shopping is the cause of the post-recession fall off in sales tax receipts from the household component, the recent expansion of the retail sales tax base to include some large e-commerce entities portends well for future growth of the household component, and a brighter future for retail sales tax overall.

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What exactly is this data?

Our data on sales tax is from Office of Tax and Revenue sales tax returns. Sales tax returns categorized as Household sales are ones with the following industry classifications: department store retailers, clothing retailers, home supplies/furnishings retailers, electronic retailers, grocery stores, pharmacies/drugstores and book stores. Business sales tax returns have the following industry classifications: office equipment/materials suppliers and services, construction equipment/materials suppliers, building maintenance services, telecommunication and energy supplier/service retailers[1], and publishers.

Data on federal spending is from the U.S. Bureau of Economic Analysis, Federal Government: Nondefense Consumption Expenditures and Gross Investment [FNDEFX], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/FNDEFX, March 14, 2017.

[1]The bulk of sales tax revenue reported by the District’s largest telecommunication and energy supplier/service retailers are from their business/industrial customers.

 

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

Nearly half of the District’s children under five are enrolled in D.C.’s Books From Birth program

The District of Columbia Public Library (DCPL) Books From Birth program mails all enrolled children in D.C. a free book each month from birth until they turn five. The program was launched by DCPL in January 2016 in partnership with Dolly Parton’s Imagination Library. The program just celebrated its one year anniversary, and we thought it would be interesting to see how the program is performing now that participation data is available.

In its first thirteen months, the Books From Birth program enrolled nearly 22,000 unique children and mailed 147,525 books. The 2015 American Community Survey estimates that approximately 40,400 children under the age of five live in the District. This translates to a 47 percent participation rate for the program – nearly half of D.C.’s under five-year-old population. We were curious to see how D.C.’s first year participation rates compare to other jurisdictions with similar programs

Shelby County, Tennessee, which includes Memphis, is an urban area that has been operating a program like D.C.’s since 2005. Shelby County has a population of 937,750 (657,167 residing in Memphis) and generally speaking has similar demographics to the District.

Shelby County

District of Columbia

Population

937,750

647,484

Under Five Population

67,000

40,400

Percent high school graduate or higher 86.9%

89.3%

Black

53%

50%

White

40%

42%

Asian

3%

5%

Latino

6%

10%

Percent Living in Poverty

21%

18%

Source: U.S. Census Bureau, 2011-2015 American Community Survey 5-Year Estimates

We plotted the first thirteen months of enrollment and participation data for the Shelby County and D.C. programs to see how they compare. The following graphs show the results of this plot. (click to enlarge)

BFB Participation Percentage

BFB Participation Number

The data shows that D.C.’s Books From Birth program outpaced Shelby County participation by about 300 percent and had more than double the number of enrolled children at the conclusion of month thirteen. The District enrolled more children in total even though Shelby County has 67,000 children under five years old compared to the District’s 40,400. We speculate that D.C.’s higher enrollment figures could be related to the fact that DCPL implemented an aggressive promotional campaign. DCPL’s campaign included posters on public transit and outreach at neighborhood festivals, DCPS parent meetings, nonprofit and government agencies, and daycare providers. Shelby County did not ramp-up its promotional outreach efforts until several years into the program and did not simplify its enrollment application until 2011. Shelby County saw swift growth in enrollment once outreach efforts were expanded. The program currently has 44,250 program participants and a 66 percent participation rate.

We also looked at where D.C.’s program participants live by using the zip code of each child’s mailing address. The top three enrolling zip codes were 20011 (Brightwood Park, Crestwood, Petworth), 20019 (Deanwood, Burrville, Lincoln Heights, River Terrace, Benning Ridge), and 20002 (Capitol Hill, NoMa, Trinidad, Kingman Park). (Click map to interact)

Enollment by Zip

The top three highest zip codes for participation rate (number of children enrolled out of the total number of eligible to enroll) were 20024 (Southwest Waterfront, Navy Yard), 20002 (Capitol Hill, NoMa, Trinidad, Kingman Park), and 20012 (Takoma, Shepherd Park, Colonial Village). (Click map to interact)

Participation by Zip

We also separated children into five buckets based on birth year to look at the age of participants by zip code. We found that the largest age cohort among Books From Birth children is newborns (under the age of one) and the smallest cohort is four-year-old children. All zip codes generally follow the same age cohort patterns except for 20018 (Woodridge, Langdon, Fort Lincoln) which had more four-year-old participants than newborns. (click to enlarge)

BFB Age by Zip

What exactly is this data?

Our data on Books From Birth participants comes from the data reported to us by the District of Columbia Public Library. This included the birth years for all participants, zip codes for mailing address, and enrollment numbers for each month of the program. We excluded zip codes with under 50 participants since many were not a physical location but rather a zip code for P.O. boxes. Excluded zip codes are included in the total enrollment and participation numbers but not the participation by location analysis.

The data regarding Shelby County was provided by the Executive Director of the Shelby County Books From Birth, Jamila Wicks.

Our data on the number of eligible children by zip code and demographics for Shelby County and D.C. comes from the 2011-2015 American Community Survey five-year estimates for number of children under five years old by zip code.

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.

(click to interact)

byoccupation

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.