tl;dr: I look at median household income within Howard County, Maryland, and how it has changed.

I’m continuing my look at median household income, in pursuit of my ultimate goal of learning more about the issues around housing affordability in Howard County.

After my previous post about Howard County median household income compared to other local jurisdictions, I now turn my attention to looking at median household income within the different parts of Howard County. Unfortunately data at the census tract level (the next level below county level as far as the US Census Bureau is concerned) doesn’t go back very far, and has some issues that make analysis more difficult.

First, median household income estimates for census tracts are not available before 2009, at least for Howard County. Second, the boundaries for Howard County census tracts changed from 2009 to 2010 (presumably as part of the work on the 2010 census). (Fortunately, the census tract boundaries have been stable since then.)

Also, figures for median household income for census tracts are available only in the American Community Survey 5-year estimates. There are no 1-year estimates available as there are for counties and cities. This means that the 2010 median household income estimates published for Howard County census tracts actually reflect income surveys in the years 2006–2010, the 2011 estimates reflect surveys in the years 2007–2011, and so on. For this reason the US Census Bureau recommends not comparing 5-year estimates from overlapping sets of years, for example comparing 2011 5-year estimates to 2010 5-year estimates.

I’ve therefore chosen to compare only the 2010 and 2017 5-year estimates, in order to get both the earliest and latest comparable data.

Next, the margins of error for median household income at the census tract level are very high, typically 10–20% or even higher relative to the base income figures. That means that ranking individual census tracts based on their median household income doesn’t really make sense, given that the relative positions of tracts on the list will in large part reflect random measurement errors.

Finally, there are 55 census tracts in Howard County, one for every 6,000 people on average. (The 2018 ACS population estimate for Howard County is about 323,000 people.) Graphing information on individual census tracts makes for a cluttered graph, unless you either plot data on a map or group census tracts together in some way.

In the map above I did both: I took the 55 census tracts and divided them into 5 groups or “quintiles” of 11 census tracts each, based on their median household income 5-year estimates for 2010. Quintile 1 contains the 11 census tracts with the lowest median household incomes and quintile 5 contains the 11 census tracts with the highest median household incomes, with the other quintiles containing tracts with income values intermediate between the lowest and highest.

I then plotted the census tracts on a map of Howard County, with each tract colored according to the quintile it’s in.¹ The results are as one might expect: the census tracts with the lowest median household incomes tend to be in eastern Howard County and/or in Columbia, while the tracts with higher median household incomes tend to be in western Howard County.

How did the median household income for the various census tracts in Howard County change from 2010 to 2017—or, more correctly, from the 2006–2010 timeframe to the 2013–2017 timeframe?

The graph above shows one way to look at this: I took the income quintiles from above, computed the averages of the inflation-adjusted median household incomes for the census tracts in each quintile, and graphed the results for 2017 compared to 2010. Thus, for example, for the census tracts in quintile 1 (the lowest-income quintile) I computed the average from the 2010 5-year estimates of inflation-adjusted median household income for all of those 11 census tracts. I computed similar averages for the other quintiles, and then repeated the process using the 2017 5-year estimates.

As a side note, averaging the median incomes in this way is not strictly speaking correct. The correct method would be to aggregate all the individual household incomes for all the tracts in a quintile, and then compute a median household income for the quintile overall. However this is not possible because the US Census Bureau does not release data for individual households within a census tract. Averaging the median household incomes for the tracts is the next-best thing.

There’s another subtlety here as well: Many graphs showing changes between income quintiles over time actually reflect different sets of people, households, or geographies between the different timeframes. For example, if in the graph the 2010 figures were to reflect the quintiles as they exist in 2010, and the 2017 figures were to reflect the quintiles as they exist in 2017, then we would not necessarily be comparing like to like. That’s because a given census tract may have moved from one quintile to another over time.

To avoid this problem I had the 2017 values in the graph reflect the income values for the same sets of quintiles as for the 2010 values: if a census tract were in, say, quintile 3 in 2010 then it was counted as part of the same set of tracts for 2017.

Take-aways from this graph (and the underlying data, not shown here) are as follows:

• The Howard County census tracts in the lowest quintile in 2010 (i.e., those with the lowest median household income) had a substantial increase in average (real) median household income between the 2006–2010 and 2013–2017 time frames, amounting to about a $7,000 increase in real terms or a 9% increase in percentage. This was actually the largest increase of any of the quintiles in both absolute and percentage terms.
• On the other hand, the census tracts in the next-to-lowest quintile in 2010 (quintile 2, just above quintile 1) experienced the largest decline in average (real) median household income between the two time frames in both absolute and percentage terms, about $6,000 or 5%.
• The census tracts in the highest quintile in 2010 (quintile 5, the 20% of census tracts with the highest median household income) had a slight decrease in average (real) median household income between the two time frames, about $5,000 or 3%.
• Finally, the census tracts in the other higher-income quintiles (quintiles 3 and 4) had slight decreases in average (real) median household income.

The first item above is somewhat counterintuitive: does it mean that relatively less affluent people in Howard County actually did better income-wise between 2006–2010 and 2013–2017 than more affluent people? That’s one possible interpretation, but not the only one, and I suspect not the most likely one.

Remember that even though the census tracts may not have changed between the 2010 and 2017 5-year estimates graphed above, the people living within those census tracts did not necessarily remain the same. In particular, it’s possible that many people living in the least affluent census tracts experienced a decline in household income so severe that they could no longer afford to live in Howard County.

Under this hypothetical scenario the people who left would presumably then be replaced by people with slighter higher incomes who could afford to live in Howard County. As a result of this turnover the median household income of these least affluent census tracts would then increase, because the least affluent residents of those tracts would have moved to other counties.

This hypothetical scenario would also explain the difference between the experiences of the quintile 1 census tracts vs. the quintile 2 census tracts: Unlike those in quintile 1, the people in the quintile 2 tracts were presumably not “on the bubble” in terms of their being able to afford to live in Howard County. They may have suffered declines in household income between the two time frames, but those declines were not so large as to force them to leave the county.

This next map shows changes in median household income between the 2010 and 2015 5-year estimates for each census tract, expressed in percentage terms. Again the calculated changes are based on median household incomes in constant 2017 dollars, so they reflect real changes rather than changes due to inflation.

Harking back to the discussion above, note that two of the census tracts with increases in median household income were those in Elkridge east of I-95 and north of MD 100. These were in quintile 1 (lowest 20% of all tracts by income) in 2010. On the other hand, the census tract east of I-95 between MD 32 and MD 175, which was in quintile 2 in 2010, experienced one of the largest declines in median household income.

This final map, like the first map above, shows Howard County census tracts in the different income quintiles, this time based on the 2017 ACS 5-year estimates for median household income instead of the 2010 estimates.

Note that of the three census tracts discussed above, the first two moved up from quintile 1 (as measured by the 2010 estimates) to quintile 2 (as measured according to the 2017 estimate), while the third moved down from quintile 2 to quintile 1.

I’m done with looking at median household income, at least for now. In my next post I’ll turn my attention to median home values.

Further exploration

For more on how I created the graphs above, see the following:

• “Howard County median household income trends by census tracts” shows the R code used to produce these and other graphs.
• My hocodata code repository includes copies of the R Markdown files for this and another analyses. (Look in the “affordability” subdirectory.)
• If you sign up for a free account on the Rstudio.cloud service you can open and make a copy of my hocodata project for this and other analyses, and try your hand at it yourself. (Again, look in the “affordability” subdirectory, and check out the RStudio primers to learn how to use the system.)

UPDATE: I’ve corrected my comments about ranking of counties to make it clear that the rankings reflect only counties and county-equivalents with populations over 65,000.

tl;dr: Looking at median household income in Howard County, Maryland, over time compared to other local jurisdictions.

I’m continuing my look at median household income, in pursuit of my ultimate goal of learning more about the issues around housing affordability in Howard County.

After my previous post about Maryland median household income I now turn my attention to looking at Howard County specifically. Unfortunately US Census Bureau data on median household income at the county level does not go back nearly as far as state-level data. The earliest county-level data I can find dates from 2005 and the beginning of the American Community Survey.

The graph above shows all the data I could find on Howard County median household income, compared to a select set of other jurisdictions. All values are in 2017 dollars. The gains and losses thus represent gains and losses in real terms after adjusting for inflation.

I chose the other jurisdictions as follows:

• Howard County has traditionally been compared with Loudoun County, Virginia, as the most affluent counties in Maryland and Virginia respectively. For this graph I also added Stafford County, Virginia, a rapidly growing county that straddles I-95 south of D.C. just as Howard County straddles I-95 north of D.C.

• I paired Montgomery County and Fairfax County, the largest and most affluent of the close-in suburban jurisdictions.

• I paired D.C. and Baltimore city as the respective urban jurisdictions of the Washington-Baltimore metro area.

• Finally, I added Anne Arundel County as one of Howard County’s most affluent neighbors in Maryland.

(I would have also added Baltimore County and perhaps Frederick County, but I ran out of colors and didn’t want to make the graph more cluttered than it already is.)

Here are some immediate takeaways from the graph above:

• Howard County experienced a significant drop in median household income from 2016 to 2017.

• Northern Virginia continues to outpace central Maryland when it comes to median household income, with Loudoun County still way out in front, Fairfax County continuing to lead Montgomery County, and Stafford County having caught up to Howard County.

• Similarly the District of Columbia is widening the income gap between itself and Baltimore city, and narrowing the gap between itself and its suburbs.

As to why these trends are occurring, I haven’t done enough research to have a solid opinion. However I will note that median household income for the Northern Virginia suburbs is increasing even as median household income for Virginia as a whole is stagnant or decreasing. This is presumably due to the rest of Virginia suffering economic problems to which Northern Virginia is immune.

This graph repeats the previous graph in comparing Howard County to other jurisdictions, except that here the measure is median household income for Howard County, etc., relative to US median household income. My takeaways here are as follows:

• Loudoun County has separated itself from the pack in the last ten years, with a median household income now 225% or more of US median household income.

• D.C. continues its growth in median household income, and is now above 130% of US median household income. Given that Baltimore city median household income is stagnant at about 75-80% of US median household income, the next few years could see D.C. have almost double the median household income of Baltimore city.

• Howard County and the other jurisdictions are trending steadily at 150-200% of US median household income.

The final graph shows the ranking of Howard County over the years versus the most affluent local jurisdictions. The story is similar to that from the graphs above.

UPDATE: While the relative rankings below are correct, the absolute rank numbers do not account for any affluent counties or county-equivalents with populations under 65,000. That’s because they are based on the American Community Survey 1-year estimates, and such estimates are not done for smaller counties.

• Loudoun County has maintained its position for the last ten years as the most affluent US county as measured by median household income, with Fairfax County also consistently in the top ten. They have now been joined by Stafford County, although the margins of error for Stafford County estimates are so large that it’s likely a matter of sheer randomness whether Stafford County is in the top ten or just outside it.

• Howard County has dropped out of the top five and the top ten, and Montgomery County has dropped out of the top ten. (Again, due to margins of error Howard County is probably in reality roughly tied with Stafford County.) Anne Arundel County was never in the top ten counties by income, and now sits at #30.

Looking at the rankings for other jurisdictions, Virginia has four jurisdictions in the top ten (with Arlington County joining Loudoun, Fairfax, and Stafford), with the remaining top ten counties in California and New Jersey (with three each). Looking beyond the top ten, ranks #11-30 include two more Virginia jurisdictions (Prince William County and Alexandria city) and five Maryland counties (with Calvert and Charles counties joining Howard, Montgomery, and Anne Arundel).

Caveats aside, overall this reinforces the story of northern Virginia’s economic success and suburban Maryland’s relative economic decline.

In my next post I’ll turn my attention to median household income within Howard County itself, looking at Census data by census tract.

Further exploration

For more on how I created the graphs above, see the following:

• “Median household income trends for Howard County, Maryland” shows the R code used to produce these and other graphs.
• My hocodata code repository includes copies of the raw data files and R Markdown files for this and another analyses. (Look in the “affordability” subdirectory.)
• If you sign up for a free account on the Rstudio.cloud service you can open and make a copy of my hocodata project for this and other analyses, and try your hand at it yourself. (Again, look in the “affordability” subdirectory, and check out the RStudio primers to learn how to use the system.)

tl;dr: Looking at median household income in Maryland over time compared to DC and Virginia.

With so much attention paid recently to the issue of housing affordability in Howard County, together with related concerns about county taxes, developer impact fees, the school system budget, adequate public facilities, and so on, I decided to take a look at these issues myself.

Instead of throwing some opinions out there without any support, I’m first taking a look at what data I can find that’s relevant to the issue, starting with data on household income and how that’s changed over time. After all, household income, along with house prices, is a key factor in how affordable Howard County is, both to existing residents and those hoping to move here.

I’d really like to look at income data specifically for Howard County, but unfortunately I can’t find any US Census Bureau income data at the county level until the 2000s. I’m therefore starting with data on median household income at the state level.

Recall that “median” means half of all households have less income than this value, and half more. Median income is a better measure than average income, which can be skewed by great inequality, especially at the high end of the income scale. And household income is a better measure than per capita income, since almost by definition it’s households that buy houses.

Above you can see the data over the last thirty years or so for Maryland, DC, and Virginia, as well as for the US as a whole. These values are inflation-adjusted, expressed in 2017 dollars. I also used the 3-year average to smooth out spikiness in the values, whether due to sampling issues or year-to-year economic fluctuations. Thus the figure for (say) the year 2000 is actually the average for 1998, 1999, and 2000, and the latest value for 2017 also reflects income levels in 2015 and 2016.

Here are some quick take-aways from the above graph:

First, at least for the past thirty years Maryland median household income has always been higher than that for the US, as well as higher than median household income for both DC and Virginia (although DC has been quickly closing that gap).

Second, US median household income has not increased very much in real terms over time: less than $10,000 in 2017 dollars over thirty years, or about $300 per year. Virginia’s median household income has risen even more slowly than that.

Using median household income as a measure of household affluence has been criticized because it does not include non-salary benefits like employer-paid health insurance. This may make the relative stagnation of US median household income less serious of a problem than it otherwise appears. However there’s no question that Virginia households overall have done less well than those in Maryland and DC over the last thirty years.

To get a better feel for how the local jurisdictions have fared relative to one another and to the US as a whole, here’s another graph showing median household income for Maryland, DC, and Virginia as a percentage of the US median household income (the horizontal line at 100%). Again, this uses the 3-year averages for median household income.

My take-aways from this graph:

First, Maryland median household income has been between 120 and 130% of US household median income for most of the past thirty years. It reached its high point in the early part of this decade, but has suffered a relative decline in the past few years. In looking at the first graph above, it appears that the relative decline has occurred because recently US median household income has been rising faster than Maryland median household income.

Second, Virginia median household income has been between 110 and 120% of US household median income for most of the past thirty years. Like Maryland, it reached its high point in the early part of this decade, but has suffered an even sharper relative decline in the past few years.

Finally, DC has undergone a startling change from the 1990s, when its median household income was less than 90% of US median household income. Today its median household income rivals the median household income of Maryland, and if we look at the 1-year estimates (not shown above) actually surpasses it.

In general median household income in a jurisdiction can rise either because existing households become more affluent, or because less affluent households leave the jurisdiction and are replaced by more affluent ones. I haven’t done any research on what’s happening in DC with respect to in- and out-migration, so I can’t say which factor might have been more important.

People love to see how they’re doing in national rankings, so here’s a graph showing how Maryland, DC, and Virginia have ranked against the other US states¹ over the past thirty years. For consistency I have used the same 3-year averages for median household income, although note that news articles about income rankings will almost always be based on the 1-year estimates.

Maryland has been in the top five states for almost all of the past thirty years, and has been number one in the rankings in recent years. Virginia has consistently been in the top ten states by median household income, although it has recently dropped out of the top ten.

Finally, DC has gone from the bottom ten to the top ten in the span of twenty years. In the rankings based on 1-year estimates it is now ranked number one, higher than any US state.

Further exploration

For more on how I created the graphs above, see the following:

• “Maryland median household income over time” shows the R code used to produce these and other graphs.
• My hocodata code repository includes copies of the raw data files and R Markdown files for this and another analyses. (Look in the “affordability” subdirectory.)
• If you sign up for a free account on the Rstudio.cloud service you can open and make a copy of my hocodata project for this and other analyses, and try your hand at it yourself. (Again, look in the “affordability” subdirectory, and check out the RStudio.cloud primers.)

NOTE: This article was originally published in my Civility and Truth Substack newsletter. I have republished it here without changes.

Michael Munger, a libertarian economist at Duke University, recently published a book Tomorrow 3.0: Transaction Costs and the Sharing Economy , in which he discusses how lowered transaction costs are changing the economy by opening up new models for employment and production. I haven’t read the book yet (though I plan to), but I have read a number of Munger’s articles on this topic, including the recent “Maybe Bosses Do Wear Bunny Slippers”. In it he highlights the example of film-making in Hollywood in support of his argument:

… “studios” now are distributors, and movies are made by “gig” workers, hired for the duration of the shooting of the film. There are about 150 different disciplines involved in making a movie, ….

If you make a movie, you go to LinkedIn and choose one of each of these workers. On the first day of shooting, the team works well together because the refinements of division of labor in the industry are clear and well- organized. After the film is completed, the gig is over.

I’ve always found Munger’s thoughts on transaction costs and the gig economy interesting, so I thought it would be fun to interrogate his argument further, specifically with respect to this example:

First, a film is a self-contained product that once created and released requires no ongoing support other than the work of licensing it for distribution through various channels. The model of bringing a bunch of different people together for a limited period of time and then having them disperse again seems very compatible with this. Would the same strategy work for products that require ongoing support, especially products sold in the business-to-business market? Or could it be made to work in those cases? (Or, closer to the original example, how is the Hollywood employment model modified, if at all, for long-running projects like multi-year TV series?)

Second, with the Hollywood model there has to be some way of determining that the person you’re hiring for this temporary film-making gig is actually competent, won’t be a disruptive force on set, etc. Is this done purely through LinkedIn-style online references, or is it a function of ongoing personal relationships and reputations built up among people working in the same (relatively small) community over long periods? If the latter, how far could the Hollywood model be extended, and how well would it work, in an environment where transactions are more anonymous and online reputations can be more easily gamed?

Finally, although workers in Hollywood don’t necessarily work long-term for a single employer, a lot of them (most of them?) do have a long-term relationship with an institution, namely their labor union. How much of the success of the Hollywood employment model depends on the presence of those unions? My quick take is that unions might provide an ongoing nexus for training and reputation-building and some protection against exploitation of workers, but could also lock into place a relatively strict division of labor that might hinder innovation. I get the impression that people who do VFX work aren’t unionized, but also that VFX work is organized at the level of firms and not at the level of individual contractors. If so, why is that? Just historical contingency? Or something in the nature of the work, for example the need to maintain a critical mass of capital, e.g., in the form of proprietary in-house-developed VFX software?

I don’t have answers ready to hand to the questions above. But from my naive perspective the Hollywood example that Munger touts seems a long way from the idealized “want a hole? rent a drill” model that he sees taking over the world due to lowered transaction costs.