Quito, en casa

I celebrated my 30th birthday this past March. In honor of the commencement of my fourth decade, Megan planned a two-week trip to Ecuador: a week and a half on the Galápagos Islands, bookended by a total of five days in Quito, Ecuador’s capital. As presents go, this one was pretty perfect, expertly calibrated to my love of weird animals, big cities, and old buildings.

Unfortunately, our trip happened to coincide with the outbreak of coronavirus cases in the western hemisphere, to which the Ecuadorian government responded with surprising alacrity. Within three days of our arrival, not only had the city been put under lockdown, travel between states was restricted and flights out of Quito (at least to the United States) were canceled. We never made it to the Galápagos Islands, and instead ended up spending nearly a week confined to hotel rooms and losing a ton of money on canceled flights.

Our journey home was an adventure in its own right. But that’s another story. Instead, here are some pictures and observations of Quito from our first days there.


The most startling attributes of Quito are geographic. For someone coming from a New England valley in late winter, it’s practically a hostile environment. Start with the elevation. At 9,350 feet in altitude, Quito is either the first, second, or third highest capital city in the world, depending on whether you count Lhasa, Tibet or La Paz, Bolivia. It’s about 77% higher than Denver and 135 times more elevated than my home base of Springfield, MA. (Mesa Verde in Colorado, where we camped last summer, comes pretty close at its peak of 8,572 feet.)

At that height, even normal activity can be tiring. The elevation is sufficiently extreme to have put selection pressure on the native population, which has evolved its way around altitude-induced hypoxia. The rest of us are left to deal with the constant fatigue, thirst, and hunger that accompany life in the mountains.

While it’s very far from the Earth’s core, Quito is very close to the equator—about 16 miles south therefrom. Its latitude further thins the atmosphere, making Quito a great place to catch a sunburn, even on a cloudy day (of which there are many).

In addition to its geography, Quito is remarkable for its size. The sprawling city is home to some two million residents. Before the lockdowns, Old Quito was buzzing with tourists and merchants selling avocados, apples, flowers, fedoras. The familiar beats of 90’s American hip hop anthems pumped out of disembodied car stereo systems, providing the soundtrack to our walks up and down the cobblestone hills of the city.

Like much of Latin America, Quito is a mix of disrepair and ornate splendor. Many of its buildings are crumbling, have broken windows, or are otherwise dilapidated. But adjacent stand a few that are kept pristine, seemingly unchanged from their colonial heyday. The grandest buildings are churches and federal offices.

Unlike in the US, God and the state seem to commingle freely in Ecuador. Both loom large over everyday life: National flags decorate the inside of La Compañía de Jesus, an insanely beautiful church. The massive Virgen de El Panecillo, a collaboration of local government and religious leaders, casts its shadow over the (apparently quite dangerous) neighborhood from which it derives its name.

Sadly, this is where the post ends. Shortly after we visited La Compañía de Jesus, mandatory quarantines went into effect. The formerly bustling city of Quito folded in on itself, its streets emptied. From behind shuttered windows, people tweeted #QuitoenCasa.


Some bonus pics I couldn’t fit coherently into the post:

Thoughts on Marc Andreessen’s IT’S TIME TO BUILD

Way way back in April of 2020, a venture capitalist named Marc Andreessen wrote an all-caps exhortation to western (particularly American) institutions and individuals: IT’S TIME TO BUILD. It’s a quick read, so I do recommend it. If that’s out of the question, you can get the gist from the opening paragraphs:

Every Western institution was unprepared for the coronavirus pandemic, despite many prior warnings. This monumental failure of institutional effectiveness will reverberate for the rest of the decade, but it’s not too early to ask why, and what we need to do about it.

Many of us would like to pin the cause on one political party or another, on one government or another. But the harsh reality is that it all failed — no Western country, or state, or city was prepared — and despite hard work and often extraordinary sacrifice by many people within these institutions. So the problem runs deeper than your favorite political opponent or your home nation.

Part of the problem is clearly foresight, a failure of imagination. But the other part of the problem is what we didn’t *do* in advance, and what we’re failing to do now. And that is a failure of action, and specifically our widespread inability to *build*.

We see this today with the things we urgently need but don’t have. We don’t have enough coronavirus tests, or test materials — including, amazingly, cotton swabs and common reagents. We don’t have enough ventilators, negative pressure rooms, and ICU beds. And we don’t have enough surgical masks, eye shields, and medical gowns — as I write this, New York City has put out a desperate call for rain ponchos to be used as medical gowns. Rain ponchos! In 2020! In America!

Marc Andreessen, “IT’S TIME TO BUILD”

Andreessen’s blog post is very good, even if it’s mostly an extended rallying cry. I think it was also very timely, as it alludes to a few subtextual themes I’m seeing come up more and more in politics:

  1. The US economy is increasingly concerned with rent extraction and distribution as opposed to genuinely productive economic activity, the latter having been off-shored to a great extent. The dollars-and-cents economic benefits of doing so aren’t really up for debate, but in social and political terms, the trade-off is looking less appealing these days. Prediction: interest in industrial policy is going to (continue to) increase among the right and possibly the left.
  2. Proceeding from a default assumption of capital scarcity is maybe not a smart way to make policy anymore. We are awash in money and not averse to printing more or deficit spending when the mood strikes. Obviously there’s a limit to how long you can get away with stuff like that, but if we can fight endless wars perhaps we can also fix a few roads.
  3. Maybe democracy is the problem? Others responded to Andreessen’s blog post by pointing out that there are political impediments to building as aggressively as Andreessen would like. Vox’s editor in chief, Ezra Klein, writes that American institutions public and private have become “vetocracies,” meaning that they’re biased against action instead of in its favor. Similarly, Steven Buss notes in Exponents Magazine that entrenched interests have captured regulators, making building, in many cases, illegal. Homeowners, for example, are hostile to development and form a powerful local political constituency.

    The thing is… isn’t this basically just policymakers being tuned into the desires of their constituents—or at least those inclined to make their voices heard? The only people who care enough to show up at a zoning meeting are the homeowners who don’t want the high-rise going in across the street. Professions lobby to be licensed so as to increase their income and limit competition, but members of the public generally don’t care enough to show up at the state house with a pitchfork.

    This is just the way it’s going to be, so maybe the answer is a system that doesn’t particularly care what its constituents have to say—or at least cares less in areas prone to regulatory capture.
  4. Finally, America’s ailments extend beyond the realms of economics and technocratic governance. Ours is a crisis of imagination, spirit, and mythology, exacerbated by the collapse of social capital across much of the nation. Consider the following anecdote1:

    In 1869, a businessman named George Atwater set out to install a network of rails throughout the city of Springfield, MA—from where I write presently—on which horses would pull carriages, a pre-electric trolley system. It seemed like such a ridiculous idea the board of aldermen laughed as they gave him permission and mocked him with an “initial investment” of eleven cents.

    Atwater built it anyway, and it turned out to be a huge success, expanding throughout the city and surpassing an annual ridership of 1 million by 1883. In 1890, less than a decade after the first electric power stations were built, the Springfield rail system began electrifying routes. By the next summer, all lines had been converted from horse to electric power. By 1904, ridership was 19 million; by 1916 it was 44 million.

    All of this—bold, successful investment in infrastructure, the rapid adoption of new technology, reliable and profitable public transportation—is technically possible today, yet this story could never take place in 2020. The aldermen would have dragged their feet, insisted on handouts to favored constituencies, and requested a handful of impact studies. Atwater would have stuck his investment in the stock market. The story would not have taken place here, because Springfield, like many former manufacturing cities, is in many ways a husk of its formerly productive self. Atwater would have lived in San Francisco, Boston, or New York.

Andreessen is right. It’s time to build. But let’s go broader than that: It’s time for a general return to alacrity in the public and private spheres, particularly for those of us who don’t live in one of the nexuses of the new economy. It’s time to rebuild social capital. It’s time to turn off autopilot.

Let’s fucking go.

###

  1. I came across this story in Lost Springfield, a local history book by Derek Strahan, who blogs at lostnewengland.com. I really enjoyed the book, so if you’re interested in the region’s history, I’d check out Strahan’s work.

Massachusetts Cities and Coronavirus

Last week, I put together a few graphs of Massachusetts covid-19 county cases and correlates. Unfortunately, working at the county level didn’t create many data points, so there wasn’t much insight to be gained. I ended the blog post with a wishy-washy pledge to maybe try to compile a city-level data set.

Well, I didn’t do it. But the Massachusetts Department of Public Health did! Starting April 15, the DPH began tracking coronavirus cases in Massachusetts’s 351 towns and cities. I took those case counts and paired them with demographic and economic data from the 2018 American Community Survey to see if any patterns emerged.

Due to some quirks of the Census, it would have been really tedious to get the data I wanted for every town and city. I ended up opting for a shortcut of sorts — using cities and towns that were their own Census Designated Place. (Don’t ask.) After all was said and done, I was left with 54 cities and towns that I could pair with data from the 2018 American Community Survey.

This data set is biased toward larger areas, which probably also means younger and more diverse areas too. And, of course, all cities and towns in the set are from Massachusetts, which probably introduces other biases.

Anyway, here’s what I’ve found so far:

(Note: I’m using log-scale to condense case counts and, in the case below, population. Boston, for example, has over 4,000 cases [log(4,000) = 3.602], while Springfield has about 600 [log(600) = 2.778].)

Log cases are best predicted by log population. This is pretty much common sense: more people means more vectors for disease, and usually denser population. A regression summary is in the caption below the graph.

log(Cases) = 1.3472 * log(population) – 4.0471
R^2 = .7526
p-value < 2.2e-16

After population, race is the next-best indicator of case counts. I’d expected — based on media reports and the word of local officials — to find a relationship between the percentage of black or Hispanic residents and log cases. But that didn’t really show up. Instead, the proportion of residents that are non-Hispanic whites has the best linear relationship to log cases — and the only one with a negative slope. The regression summary statistics in the caption are only for the white-log case relationship.

log(cases) = -1.8623 * white + 3.5474
R^2 = .545
p-value: 1.887e-10

This does actually fit the narrative pretty nicely if we lump all non-white ethnic groups together mentally. But as I noted in the last post, race is collinear with so many other variables that it’s hard to know what we’re seeing.

There is, however, at least some indication that what we’re observing might really be about race. Other variables we would imagine to correlate to race and population have much weaker relationships to case counts. (“Public transport” is the percentage of people who take public transport to work, and “Poverty line” is the percentage of people below the poverty line.) I grabbed a bunch of variables like these, but so far, none of them are very helpful in explaining what’s going on.

Combining log population and the proportion of residents who are white gives us an adjusted R^2 of .8209, which is nice. But when I tried to use that model to predict case counts for four municipalities that weren’t among the 54 I’d defaulted into working with, it only did okay.

The problem, I think, is that the selection of cities in the data set I’m working with is biased toward larger areas. It’s also possible (in fact, likely) that there’s both more to the story and that an element of randomness is at play, too. Aside from age, it’s been hard for professionals to isolate significant risk factors.

A few MA covid graphs

This is a low-stakes post.

Massachusetts has been releasing county-level coronavirus case counts, which I paired with data from the US Census to look for patterns. I actually didn’t end up finding anything particularly interesting, but some of the graphs are nice, so I thought I’d share.

On case growth

A few days ago, it looked like the growth of covid cases in Massachusetts might be flattening. But as of yesterday, it seems like that’s not quite the case across the board. Here are the total case counts per 1,000 residents of each county county since March 15:

Dukes and Nantucket counties omitted.

And here are cases per 1,000 residents on April 7, with the geometric growth rate of cases over the last week indicated by color:

Growth rate calculated as (x_1/x_0)^(1/7)-1

Berkshire, Barnstable, and Franklin counties have the lowest case growth rates, ranging from 6.2% to 7.3% on average per day over the last week. These counties have some common characteristics:

  • They’re geographically remote;
  • They are the only MA counties to have experienced population decline over the last decade;
  • They have the highest non-Hispanic white populations per capita and the least foreign residents;
  • They have the greatest proportion of residents over 65 (at least 22% in each case!);
  • Franklin and Berkshire counties have the lowest population densities, at 102 and 141 people per square mile, respectively.

To me, the above is consistent with the idea that economic activity is a vector for the spread of coronavirus (not literally, but it gets people in contact which causes person-person transmission).

Plymouth, Hampden, and Bristol are the counties with the fastest-growing case counts, each of them averaging an increase of over 11% daily over the last week. These counties don’t have much in common, so I’m having trouble putting together a potential unifying narrative.

Race and population density as correlates

It’s starting to look like black Americans might be more susceptible to coronavirus than other racial and ethnic groups. At first glance, that appears to show up in county-level data. But upon closer examination, that doesn’t appear to be the case — first because population density and the percentage of black residents are collinear, and population per square mile has a higher correlation coefficient; and second because Suffolk county (Boston) is influencing the linear relationship in both cases. Adjusted R-squared drops heavily if we exclude Suffolk county from the data set. (Race and population density were the best predictors I found of cases per 1,000 residents.)

This isn’t to say race and its many correlates aren’t good predictors. I think it speaks more to the (severe) limits of the data set I’m working with. If I have time, I may try to build a city-level data set. If anyone knows of one (or something better), link me!

Should we be surprised that “young people” make up a substantial amount of coronavirus cases?

I’ve noticed media outlets are reporting that young adults make up a significant share of coronavirus cases with an air of incredulity.

My local paper posted on Facebook that “More than 50% of coronavirus cases in Massachusetts are people under the age of 50.” Very similarly, the Pittsburgh Post Gazette writes that “more than half of Pennsylvania’s confirmed COVID-19 patients are under 50 years old.” The New York Times, for its part, reports that “nearly 40 percent of patients sick enough to be hospitalized were age 20 to 54.” *

I can’t decide if this is a psyop to get young people to take the epidemic more seriously (as numerous spring-break photos show they should) or genuine surprise. If it’s the latter, I’m not sure if that’s warranted.

In each case, the age ranges in question are massive and not very meaningful without comparison to the age distribution of the general population. For example, in Massachusetts, about 63% of the population is under the age of 50. So if the incidence of coronavirus were age-independent, we might actually expect more cases among people under 50.

I think the issue, then, is that people seem to be assuming prevalence of the virus should be age-dependent to a higher degree than we’re observing. Maybe there’s a good Bayesian case to be made for this null hypothesis; I don’t know. But I feel like laypeople — local papers included — ought to be preceding with the assumption of age-independence, especially because we still don’t have much information.

Also, what’s going on with the under-20 crowd, which makes up 23% of the population but only 2.2% of MA coronavirus cases? Is Gen Z+ holding out on us?

The typo in the y-axis comes from the MA DPH data set, which has decided there are no 19-year-olds in the Bay State.

* This isn’t as egregious as the other two examples. It’s still a huge age range: about 48% of America is between the ages of 20 and 54. But since we’re talking about the severity of symptoms and hospitalizations, it seems much more noteworthy.