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         

Resident employment grew four times faster in DC than in the suburbs over the past 4 years

According to the US Bureau of Labor Statistics, the number of employed DC residents rose from 323,823 in April 2012 to 370,204 in April 2016, a 14.3% increase of 46,381. This increase stands out in the context of recent labor market trends in the US and in the Washington metropolitan area:

—The percentage increase was more than twice that in both the US economy (6.6%) and four times the increase in the DC suburbs (3.5%),

—The increase represented almost one-third of the increase in the entire metropolitan area, although DC’s regional resident employment share is just under 12%.

—The percentage change in DC’s resident employment was more than twice the increase in jobs located in DC. (14.3% v. 6.2%). For the US and the rest of the metropolitan area, resident employment actually grew a little less rapidly than wage and salary employment.

—The increase, averaging 11,595 per year, is about equal to the growth in DC’s population over that period.

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Three places to look in helping to explain this remarkable increase in employed DC residents are: (1) growth of wage and salary jobs in DC, (2) unemployed persons returning to work, and (3) labor force growth. As noted below, all of these have contributed, but the most important explanation lies with labor force growth and related dynamics, particularly population growth.

Wage and salary employment located in DC. DC employers added 45,467 wage and salary jobs from April 2012 to April 2016, about the same number of jobs as the increase in resident employment. These new jobs could certainly be a source of employment for additional DC residents. Although DC’s jobs grew a little faster than those in the suburbs, there was, however, nothing very unusual about this increase. DC’s share of the new jobs in the metropolitan area over the past four years (26.6%) was close to its recent average share of all metropolitan area jobs (about 24%).

 

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A growing job base no doubt helps to attract workers to the District of Columbia, but job growth in DC cannot explain why employed residents grew by 14.3% while jobs grew 6.2%. It should be noted that from April 2012 to April 2016 the percentage increase in resident employment (6.6%) in the US economy was actually a little less than the 7.5% wage and salary job growth, and the Washington metropolitan area growth pattern was similar, albeit a little slower—4.6% for resident employment and 5.6% jobs.

 

Unemployment. DC unemployment declined by 8,481 from April 2012 to April 2016, which represents about 18% of the increase in resident employment. However, falling unemployment cannot explain why resident employment increased so much faster in DC than elsewhere. DC’s percentage decline in unemployment was less than in the Washington metropolitan area suburbs and the US.

Looked at another way, over the past four years, it took an increase of 5.5 DC employed residents to reduce unemployment by one (46,381 increase in resident employment divided by 8,481 decline in unemployment). In the US the ratio was 2.1 new employed resident for every decline of one in unemployment, and in the suburbs the ratio was 1.9 new employed resident for every reduction of one in unemployment.

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Labor force dynamics. By definition, the increase in resident employment must be equal to the sum of the reduction in unemployment plus the increase in the labor force. Consequently, over 80% of the growth in resident employment is accounted for by labor force growth.

 

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Along with resident employment, the increase in DC’s labor market represents another unusual change over the past four years. The 10.6% increase in DC’s labor force was 3.7 times greater than in the US (2.9%) and more than 7 times greater than in the suburbs (1.5%). With about 12 percent of the region’s labor force, DC accounted for 46.5% of the region’s increase over the past four years.

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Population growth is the principal reason why DC’s labor force is rising so significantly. Over the past four years DC’s population grew 7.5%, compared to 4.8% in the suburbs and 3.2% in the US. If DC’s labor force had grown at the same rate as population, the labor force would have grown by 26,617. This growth in labor force due strictly to population would account for about 70% of the 37,901 labor force increase, and 57% of the 4 year increase in DC resident employment. About 30% of the labor force increase, however, is related to factors other than population growth. These factors cannot be explained by this data. For example, DC’s rising population may have an unusually large share of people in the labor force. Or the entire population may be changing so that the proportion persons in the labor force is rising. Or rising employment opportunities may be pulling more of the people who have left the labor force into DC’s labor market, although it is not obvious why this should be more true in DC than elsewhere.

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Where do the additional DC employed residents work? The BLS data used in this survey do not indicate the place of employment for DC residents. The increase in DC resident employment from April 2012 to April 2016 is the result of some combination of (1) a portion of the increase in new wage and salary jobs added in DC, (2) DC residents filling jobs formerly held by commuters who retired or otherwise left their positions, (3) additional DC residents commuting to the suburbs, and (4) additional DC workers who report they are working but are not as wage and salary employees.

The importance of commuting patterns is underscored by trends in suburban jobs and resident employment over the April 2012 to April 2016 period. During those four years suburban resident employment growth was far below the percentage change in jobs located in the suburbs (3.5% v 5.4%), and the increase in wage and salary jobs exceeded the growth of resident employment by more than 30,000. This difference between job growth and resident employment growth in the suburbs would appear to provide employment opportunities for DC residents commuting to the suburbs—and employment opportunities as well for persons commuting from outside of the Washington DC metropolitan area. In addition, the relatively slow growth in the suburban labor force could indicate a slowing of interest in commuting to the District of Columbia.

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What is this data?

This analysis of labor market trends in the US and the DC area covers the period from April 2012 to April 2016, a time that includes the most recent four years of recovery from the Great Recession. (Recovery from the recession officially began in June 2009.) The analysis uses data from two Bureau of Labor Statistics surveys that are conducted each month: (1) wage and salary employment data by place of work and (2) labor market data by place of residence, which includes labor force, resident employment, and unemployment. The data for April 2012 and April 2016 are three month averages for February, March, and April. Population data for the first quarters of 2012 and 2016 are from Moody’s Analytics.

It should be noted that the data presented here can be revised as Census and BLS sort through additional information that becomes available to them.

 

 

 

Apartments growing more rapidly than population, while new office space lags employment growth

Over the past three years population and jobs in DC have grown steadily and at about the same rate. From the first quarter of 2013.1 to 2016.1. DC’s population grew a little over 34,000 or 5.3%. This percent increase is just a little bit faster than wage and salary employment. DC added almost 33,000 jobs over the period, a 4.4% percentage increase.

People need to live somewhere and they need to work somewhere, so it would be expected that both the number of apartments and the amount of office space would increase as well. The impact is much greater for apartments, however, than for office space. According to CoStar, from 2013.1 to 2016.1 the number of apartment units increased by 12,805 (7.8%), well over the percent gain in population. By contrast, the net increase of 1.22 million square feet of commercial office space represents an increase of only 0.8% over the three years, a percentage change far less than the gain in employment.

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What accounts for these different relationships? The short answer is that most of the new population lives in apartments, but the connection between commercial office space and jobs is much looser.

Apartments tracked by CoStar represent about 55% of all housing units in DC. The other units are mostly in single family or other small structures whose numbers have not increased much over the past 3 years, although some have been reconfigured for more units. The housing supply for a growing population is thus mostly in larger multi-family buildings, principally apartments.

By contrast, many of the jobs added to the District’s economy do not need office space. For example, retail and food services accounted for more than one-third of all new jobs in DC over the past three years. In addition, other jobs are located in schools, hospitals, government office buildings and other locations that are not commercial offices. The relationship between job gains and commercial office space is further weakened by the well-documented decline in the number of square feet of space needed by many office workers, reflecting factors such as telecommuting, open office layouts, and reduced need for libraries in law firms.

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Construction dynamics

Looking back over the past decade, construction trends for apartments and commercial offices reflect the business cycle as well as responses to growth in people and jobs and other market forces. As the Great Recession approached in 2007, construction of apartment units and commercial office space, measured as percent of inventory, was increasing. As shown in the following chart, construction as a percent of inventory was over 3% for apartments and about 5% for offices in early 2008. Construction fell sharply with the recession, reaching about 1% of inventory in 2010 for both apartments and offices.

With the recovery, apartment construction ramped up sharply starting in 2011, reaching 6.9% in the third quarter of 2015. Commercial office construction, however, was a very different story. It increased slowly, never getting close to the pre-recession pace. Furthermore, the majority of the office construction did not result in a net increase in office space. The primary effect of the new office construction seems to have been directed to meeting demand for improved amenities or better location. As shown in tables on the next page, over the past three years, more than 92% of the new apartments delivered to the market resulted in a net increase in inventory. For commercial office space, net inventory rose less than one-third of the newly delivered office space.

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Gender pay gap among the District’s workforce

The gender pay gap refers to the systematically lower wages women receive relative to men with similar talents and responsibilities.  A White House study tells us that the median wage for women equaled only 77 percent of the median wage for men across the nation–a pay gap of 23 percent. The pay gap exists between men and women with similar degrees, similar work experiences, and similar occupations.  The study mentions various factors that create this gap: family responsibilities such as child rearing generally fall on the women; or men tend to negotiate and seek promotions more aggressively.

But explanations offered for the gender pay gap, more often than not, do not settle the matter; rather they lead to new debates. Economists from the Federal Reserve Bank of St. Louis, for example, point out that broad comparisons are not appropriate for gender gap analysis and one must take into consideration differences in attainment, experience, and occupational choice as well as the intensity with which someone works. They argue that when one accounts for these differences the pay gap between men and women declines to five percent. There are even disagreements of the effects of childbirth: some argue that high-skilled women experience a smaller wage effect from taking time off for childbirth because of the demand on their skills, whereas others argue that the declining the declining birth rate among high-skilled, highly-paid women is evidence that childbirth hurts long-term wages.

We became curious: How about the District’s workforce? In this city with many workers on government pay schedules, do we have a gender pay gap that is significant? How does the gap change with age, marriage, education level, hours worked, or occupation? We looked at the annual wage and salary earnings of men and women.  (In this study, we use average wages, and not the median. The median salary is useful when data has very extreme values. But we are relying on American Community Survey for data, which already caps incomes and therefore throws out extreme values. Even then, the pay gap to some extent reflects the dominance of men among very highly paid workers. For example, among those who receive wage or salary income of $500,000 or more, men outnumber women by more than 3 to 1.) Here is what we found:

Overall gender pay gap

2014 data from the American Community Survey tells us, in that year, women made up 48 percent of the workforce (the nationwide share is 47 percent) and received only 42 percent of the wages. The average salary for women over the age of 21 in 2014 was $71,000 and for men it was $90,000. That is, women’s annual wage and salary earnings were 78 percent of men’s; putting the District’s gender pay gap at 22 percent compared to the national gap of 23 percent.

Gap by type of employer

The pay gap is greatest among workers of the private entities. Among for-profit firms, women, on average, receive 76 percent of the average wages paid to men; among non-profit firms, the comparable number is 77 percent. It appears that the government sector has a smaller pay gap, with a 16 percent gap among state and local government employees (largely D.C. government but also employees at charter schools, UDC, and other local entities); and the gap is 15 percent among federal government workers.

Has the gap been closing over time? To see this we look at the wage differentials among older and younger cohorts of workers. The gap generally widens by age across all four types of employers. For example, among for profit firms, the pay gap is only 7 percent for workers between the ages of 25 and 34; among the workers of non-profit firms, we don’t see a gap at all. However even when the gap is small at this age group, it does not tell us definitively that the gap will continue to be smaller when this cohort ages. This is because a small gap can widen over time even if the two groups received the same percentage increase in their wages. Viewed from this angle, it appears like the government sector, especially the federal government, has promotion practices that are most gender-blind, as the gap does not widen as quickly across cohorts.

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How about education? The data suggest that in the private sector, having a college degree (or higher) does not necessarily close the pay gap.  This is significant, but is still not a definitive indicator of gender-dependent pay or discrimination. For example, if more men study STEM and gets jobs in science and technology fields, and more women study social sciences or humanities that feed into lower paying jobs, then a pay gap will exist for given degree in education. Data do tell us that 70 percent of STEM degrees awarded at universities and colleges indeed go to men.  This we can also see at the doctoral level: even though more women receive doctoral degrees than men in a given year, women are extremely underrepresented in STEM fields; they are also less likely to get a degree in physical sciences and business programs, which tend to lead to higher paying jobs.  But, even with professional degrees (doctors and lawyers) there is a significant gap of 25 percent between men and women.

Finally, for DC residents who make up a third of the District’s workforce, the gender gap is smallest at the federal government and private, for-profit firms, and widest among DC residents employed in a state or local government job.

Is there a marriage toll for women?

Among men and women who have never been married, the gender pay gap is only 6 percent. Among men and women who are married, the gender gap is 23 percent. A single woman (whether married or divorced) between the ages of 45 and 54 makes more than a single man in the same age group. A married woman of the same age group makes 23 percent less than married man in the same age group. Why could this be? Perhaps married women in this group work fewer hours, or they took time off to raise children, or took more flexible but lower paying jobs. In fact, the gap is much smaller among married women under the age of 34 (and presumably without children).  We can think of many other possible contributors: married men are happier, and perhaps for this reason more productive. (They make more than unmarried men as well: see here and here). Because their wives take over responsibilities at home, perhaps married men can dedicate more time to their work. Perhaps there are evolutionary reasons: men who have not been picked for a marriage by the age of 45 are just low in productivity. Perhaps it is selection bias in the other direction: women who are aggressive about their careers do not get (or stay) married. Perhaps employers low-ball the salaries of married women expecting that they would take more time off at some point in their career and perhaps just leave. Again, we do not have definitive answers.

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Work hours

Gender pay gap is a feature of full time work.

When we look at men and women who work fewer than 30 hours, we see more women than men, and their wage and salary earnings are generally greater than men’s. But the size of District’s part time workforce is small—it stands at less than 10 percent of the total workforce in the District. Among those who report working 30 to 40 hours a week, the gender pay gap is 11 percent (464,000 workers report working 30 to 40 hours and half of them are women.)  Among those who report working over 40 hours (approximately 250,000 workers, 41 percent of whom are women) the gender pay gap is 21 percent.

This is a perfect example of how the same information can support exactly opposing policy solutions. One can look at the gap and see the need for interventions; others can look at it and conclude that the labor market can create many types of jobs that require different combinations of effort and wage that fit the needs, abilities or desires of all kinds of different workers. If that is the case, interventionist policies would actually reduce employment and hurt women more.

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Industry and occupations

Dominance of women in a given industry does not guarantee a better pay.  For example, 70 percent of workers in the health care industry are women.  This includes not just people with medical occupations such as doctors and nurses, but also all other types of workers from managers to computer programmers who work for a healthcare company. In this industry, the gender pay gap is 36 percent. Women in construction—a male dominated industry—make more than men but this is because they are more likely to hold managerial or creative jobs, rather than working on the construction site.

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So perhaps we should compare men and women doing the same job. Cutting and slicing the data gets precarious when we go down to individual occupation level, but we looked at occupations with more than 2500 men and 2500 women, and found only a handful of occupations where the pay gap is small or favor women. Physicians and surgeons in the District face a gender pay gap that is as wide as the healthcare industry: 37 percent! Looking at the lawyers—the gap is 73 percent. We can string together more explanations: more women go in-house (lower pay) or most women lawyers are younger.  But some other outcomes are puzzling: 63 percent of education sector employees are women; the industry gender pay gap is 23 percent. Among elementary and middle school teachers only (we are excluding administrators, and we don’t know if they are public charter school,  DCPS or private school teachers), the gap is 20 percent. Why?  What explains the gap for cashiers among whom women receive only 63 percent of what men earn? How about waiters and waitresses where the gap is 11 percent? Do they get fewer hours of work or does gender, ethnicity, or other characteristics–of the wait staff or the customers–play a role in how we tip waiters and waitresses?

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Survey data could be idiosyncratic, especially given the small size of the District’s sample (but in this case we are using data from three states). Still the pay gap is there, even when one controls for age, marital status, or even down to occupation. Is this a sign of flexibility in the labor market? If all jobs required 40 hours a week (no more, no less), had the same degree of flexibility (or inflexibility) would the labor market outcomes be better for women? Or do firms knowingly take advantage of lower reservation wages for women? If so, why do women accept lower salaries when everything tells them to negotiate? There has been research that says women can be penalized for negotiating if they negotiate like men, suggesting that gender roles continue to play a role in the workplace.

We wonder what our readers think.

What exactly is this data? We use the American Community Survey micro data for 2014. We combine District of Columbia, Maryland, and Virginia data for respondents who report working in the District. Wage and salary income is what respondents report earning in a year.

 

$26 Billion of Taxation without Representation

A recent court decision gave the District of Columbia control over its local budget for the first time since home rule in 1972. At a time when support for D.C. statehood is at an all-time high (67 percent of residents are in favor ), the court’s decision has brought a renewed optimism to the District’s 51st state movement. The Mayor and Council are taking advantage of the momentum generated by the court decision to propose a new ballot initiative and legislation to further the statehood agenda.

We all know the District’s statehood battle cry – Taxation Without Representation – but how much do we actually pay to the federal government each year? In order to answer this question, we dug into the federal tax collections data. What we found is that District residents and businesses paid a whopping $256 billion in federal taxes since the IRS began to track the District’s tax collections separately from Maryland in 2002. In 2014 the District paid the federal government $26.4 billion which is likely the largest contribution in the city’s history. The District only received $3.5 billion in federal grants, payments, and court contributions in fiscal year 2014 – a difference of roughly $22.9 billion (this excludes matching federal funds such as those we receive for Medicare, but all states receive these). Since 2002, the annual tax paid to the federal government grew by $11.7 billion (an increase of 80%).

Annual Federal Tax Payment

In 2014 the District paid more federal taxes than 22 states and paid nearly the same amount as South Dakota, Alaska, Montana, Wyoming, and Vermont combined. Those states have 15 seats in Congress while the District has only one non-voting delegate.

Annual Collections Rank

Although some states pay more federal taxes than others, it does not necessarily translate into more congressional representation. In 2014, New Jersey paid $9.63 billion of federal taxes for each of its 14 congressional seats – the highest in the country. West Virginia on the other hand only paid $1.38 billion for each of its five congressional seats – the lowest in the country. If the District became the 51st state in 2014 and had three seats in congress, it would have paid the fourth highest amount of federal taxes per congressional seat.

Federal Taxes Paid for Each Seat

What exactly is this data?

The IRS Data Book is published annually by the IRS and contains statistical tables and organizational information on a fiscal year basis. The data used in this analysis is published by the IRS annually and is available here. We used data from the Comprehensive Annual Financial Report for 2014 to obtain total revenue and federal government contributions to the District. In order to calculate federal spending on the District’s judicial system, we collected information from the District of Columbia Budget Request Act and the Budget of the United States Government for 2014.

Have you ever moved without getting your utility deposit back? Still have those uncashed checks? Or, have you forgotten about an old checking or savings account?

According to the National Association of Unclaimed Property Administrators (NAUPA), states are collectively holding on to approximately $41.7 billion in unclaimed assets, including dormant bank accounts, stock splits, life insurance payouts, gift cards and uncashed payroll checks among other funds. This is up 27% from $32.8 billion in 2010.

Consistent with the nationwide trend, unclaimed monies in the District have shown a modest increase over the course of the last 10 years. As of the end of FY 2015, total unclaimed funds in the District amounted to $300 million corresponding to approximately 1.4 million account records. The proportion of claims paid out to rightful owners has also picked up since FY 2012. Below is a chart depicting this trend.

Unclaimed Property Remittances Vs Claim Payouts

If you haven’t heard of unclaimed properties before, here is a simple description: if banks, insurance companies, credit unions, pensions and similar entities owe you money or other financial assets and you do not collect it, then it’s called unclaimed. These assets could be checking accounts, certificates of deposit, customer deposits and over-payments, gift certificates, paid-up life insurance policies, unpaid wages, insurance payments, money orders, refunds, saving accounts, proceeds of safety deposit box auctions, etc.

By law, all these entities have an obligation to remit the unclaimed property in their possession to the appropriate state(s). The idea is that the property is not theirs, and that states have more resources for finding the individual or company that has ownership. However, if no owner is found, states get to keep the property. In the District, all funds received by the Unclaimed Property Unit are deposited into the District’s General Fund. Although the money belongs to the owners, who may claim it at any time, the funds may be used in the interim to help defray the District’s operating costs.

Below are few statistical highlights that we thought would help increase public awareness of unclaimed properties in DC and nationwide.

  • In FY 2011 (also see the chart above), a record $22 million was returned to the rightful owners by the District. This was the largest total claim payment ever in a fiscal year.
  • The largest claim ever paid to an individual also occurred in FY 2011, about $1.2 million. This may also be an effect of the last recession, as people tend to look for missing monies during economic hard times.
  • The number of claims in the District has increased by 89% during the last 10 years whereas the number of accounts remitted rose by well over 100%.

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In the District, uncashed checks and forgotten account balances seem to constitute the largest sum of unclaimed properties, whereas nationwide, orphaned 401(k) accounts (401 accounts forgotten by employees after they leave their job) are reported to bring in the most.

Unclaimed Property by Type

  • In a nationwide survey done in the year 2014, the State of New York topped the list with $12 billion unclaimed funds reported statewide. Our neighboring state Virginia stood 10th during that year. Below is a table showing where DC and neighboring states stand as of the end of FY 2015.
States/City

DC

MD VA

NY

Estimated amount of Unclaimed Funds as of the end of FY 2015 (in millions)

$300

$838 $1,200

$14,000

So, if you think you might be missing money, you may start your search with www.Unclaimed.org or http://www.missingmoney.com which would direct you to the appropriate state unclaimed property website.

 What exactly is this data? The above analysis is based on historical Unclaimed Property Remittance and Claim Payment data obtained from the District’s Office of Finance and Treasury-Unclaimed Property Division. States have different unclaimed property laws that dictate how they are reported and accounted for. The FY 2015 Unclaimed Property fund balance data was obtained from each state’s Unclaimed Property Offices. The District’s unclaimed property laws are available at D.C. Code § 41-101 and § 42-201. The nationwide data was obtained from newsletters published by NAUPA and also available at www.Unclaimed.org.

 

The Increase in Higher-Income Children in D.C.

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Recently we reported that the total number of children in D.C. has been increasing since 2010 after about fifteen years of decline. Since 2008 the increase has been driven by young children under 6, and in 2012 the number of school-aged children (aged 6 to 17 years) began to increase as well.

Now we look at how the number of children in different income groups has been changing. We define lower-income as households making less than $50,000; middle-income as households with incomes between $50,000 – $150,000; and higher-income as households making over $150,000

Both higher-income households and lower-income households are driving the boom in young children under 6. The average number of children in both income groups increased between the two five-year periods for which we have reliable data: 2006 to 2010 and 2010 to 2014. The higher-income group, though, grew more.

A different story emerges for school-aged children. For children between the ages of 6 and 11, there was a net increase only among higher-income kids. The number of lower-income and middle-income children in this age group declined slightly, but because the declines are small and the data has a sampling error, we are less confident in these trends. (We discuss the reliability of the data at the end of the post.)

For older children between the ages of 12 and 17, the average number of lower-income kids declined between the two five-year periods that ended in 2010 and 2014. The data shows a slight increase in the average number of higher-income children and a slight decrease in the average-number of middle-income children, but we have less confidence in these trends since the changes are small.

Lower-income children still outnumber middle- and higher-income children of all ages. But the higher-income group appears to be catching up with the middle-income group, especially among younger children.

Is this proof of gentrification? The general trends suggest yes. When we look at children of all ages, only the higher-income group has, on net, added children. The total number of lower-income and middle-income children has remained about the same between the two five-year periods that ended in 2010 and 2014. This means that higher-income children now make up a bigger portion of all children in the city. But it could be that because we’re looking at five-year groupings of data, we’re missing year-to-year trends that could show more nuanced trajectories for children in different income groups. It’s quite possible, for instance, that the number of school-aged lower-income children has been increasing the past few years, but losses in the late 2000’s and early 2010’s have outweighed recent gains. Or it could be that the number of school-aged lower-income children has not begun to increase yet, but will soon, following a trajectory similar to that of higher-income children.

What exactly is this data?

Our data comes from the American Community Survey (ACS) 5-year data sets for 2006-2010 and 2010-2014 for the District of Columbia. We could not use ACS 1-year data sets because the small sample size makes them unreliable for this analysis.

Errors for each of the data points in our graph range from +/- 6% to +/- 11% for 90% confidence intervals. For the bolded lines in our graph, trends (population loss or decrease) still hold even if we assume the largest errors (at 90% CI) in the least favorable direction. For the non-bolded lines, trends reverse when we factor in the largest errors in the least favorable direction.

Household incomes are in 2014 dollars.