Jekyll2024-01-04T07:08:10+00:00https://danwahl.net/feed.xmldanwahl.netmaking things (better)Dan WahlSchelling Out2022-05-14T00:00:00+00:002022-05-14T00:00:00+00:00https://danwahl.net/blog/schelling-out<p>Whether or not it’s <a href="/blog/solar-panel#windy-city">windier than average</a>, my adopted hometown of Chicago is definitely not known for its balmy winters. And even though I thought I was prepared for the worst when I moved here nearly a decade(!) ago, the frigid isolation over the past two pandemic years has pushed me over the edge: I want out.</p>
<p>Not <em>entirely</em> out, of course. I still love the city, and maintain that it’s one of the finest places to spend the summer in the continental US. Not only does the city take its warm weather activities seriously (having dreamed about them all winter), but unbeknownst to many nonresidents, Chicago is a <em>beach town</em>, with 22 miles of sandy parks and trails along Lake Michigan, and within walking distance of nearly anywhere in the city!</p>
<p>There’s also the inertia: I like my Chicago apartment and my Chicago friends, and I’m not <em>quite</em> ready to reboot my life for a third time in as many decades. But I am ready to GTFO next winter. The only question is: where to?</p>
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<ul id="markdown-toc">
<li><a href="#four-factors" id="markdown-toc-four-factors">Four factors</a> <ul>
<li><a href="#winter-weather" id="markdown-toc-winter-weather">Winter weather</a></li>
<li><a href="#bike-ability" id="markdown-toc-bike-ability">Bike-ability</a></li>
<li><a href="#vegan-friendliness" id="markdown-toc-vegan-friendliness">Vegan-friendliness</a></li>
<li><a href="#housing-costs" id="markdown-toc-housing-costs">Housing costs</a></li>
</ul>
</li>
<li><a href="#aggregation" id="markdown-toc-aggregation">Aggregation</a></li>
<li><a href="#conclusion" id="markdown-toc-conclusion">Conclusion</a> <ul>
<li><a href="#results" id="markdown-toc-results">Results</a></li>
</ul>
</li>
</ul>
<h1 id="four-factors">Four factors</h1>
<p>We’ve already talked about weather, which is the primary reason for this (partial) move. But weather itself is not a sufficient criterion for evaluating destinations: I also want to participate in enjoyable activities, find (or import?) a like-minded community, and not pay through the nose for the opportunity.</p>
<p>And so, being an engineer, I went in search of some data that to analyze, and eventually landed on the following four factors: (winter) weather, bike-ability, vegan-friendliness, and housing costs. While these don’t result in a perfect approximation of a location’s desirability, they at least serve as a rough estimate, and may even reveal some hidden gems that would otherwise get lost in the shuffle.</p>
<h2 id="winter-weather">Winter weather</h2>
<p>I admit the following section is, in part, an excuse to promote a personal hobbyhorse: <a href="http://www.utci.org/">Universal Thermal Climate Index</a> (UTCI). See, there’s something fundamentally broken about the way meteorologists attempt to convey weather conditions to the public. A typical forecast will include a bevy of information: temperature, humidity, precipitation/cloudiness, wind speed, pressure, etc. It may even present niche derivatives like heat index and wind chill (which, perplexingly, are typically reported in the same units as temperature).</p>
<p>What they do not provide (and therefore expect the end-user to infer for themselves via multi-dimensional analysis) is the answer to the question that I care about most: <em>will I be comfortable outside?</em></p>
<p>As an example, take the following two real forecasts for May 15th, 2022:</p>
<ul>
<li>Seattle, WA: High of 61F, 89% humidity, 100% chance of rain, and winds from 5-10 mph</li>
<li>Phoneix, AZ: High of 106F, 5% humidity, clear skies, and winds from 10-15 mph</li>
</ul>
<p>Imagine you have to choose to spend the day in either Seattle or Phoenix–which should you prefer? Despite providing tens of bytes worth of information, the standard forecasts fail to explicitly provide the single bit that would actually answer that question.</p>
<p>So why hasn’t a metric like UTCI caught on? I’m not sure, but it might have something to do with the reference implementation, an extremely opaque ~200 term <a href="http://www.utci.org/utci_doku.php">regression model</a> (written in <em>Fortran</em>), or the variance in individual preferences, which could make it impossible to capture something “universal” along a single dimension.</p>
<p>But small details like that won’t stop me from using UTCI in <em>my</em> analysis. What almost <em>did</em> stop me was the near impossibility of finding an easily accessible source of average climate data by city, on which I could run the computation. Thankfully I eventually located a convenient <a href="https://github.com/ladybug-tools/ladybug-comfort">Python library</a> and <a href="https://climate.onebuilding.org/">data source</a> (both of which seem to be used primarily for building HVAC design).</p>
<p>UTCI is <em>also</em> reported on a faux temperature scale (why??), but at least specifies a set of color-coded ranges and associated thermal stress levels:</p>
<p><a href="/assets/images/schelling-out/utci.png"><img src="/assets/images/schelling-out/utci.png" alt="utci.png" title="utci" class="center-image" /></a></p>
<p>For the purposes of my analysis, I computed the seasonal (winter and summer) percentage of “waking” hours (from 8am to midnight) during which the weather in each city was “comfortable” (e.g. no thermal stress), yielding the following top 10 list (sorted by <code class="language-plaintext highlighter-rouge">winter</code>):</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code> winter summer
City State
Berkeley California 0.768750 0.724864
Carlsbad California 0.764583 0.513587
Costa Mesa California 0.761111 0.496603
Santa Ana California 0.740278 0.470109
Orange California 0.740278 0.470109
Oceanside California 0.732639 0.451766
Inglewood California 0.725000 0.477582
Norwalk California 0.711111 0.403533
Fullerton California 0.711111 0.403533
San Diego California 0.702083 0.454484
</code></pre></div></div>
<p><cite>Turns out California has nice weather, go figure</cite></p>
<p>An example interpretation of the above: during the winter, it is “comfortable” (no thermal stress) 72.5% of waking hours in Inglewood, California. Also note that, while you <em>could</em> use the <code class="language-plaintext highlighter-rouge">summer</code> scores if you already live somewhere hot and are looking for a “summering” destination, you should skip all that and just come to Chicago instead.</p>
<h2 id="bike-ability">Bike-ability</h2>
<p>One of my favorite things about Chicago is how bike-able it is. After moving here with a car (essential for my previous, interminable commute in DC), I quickly realized that biking was not only great for exploration and exercise, but also the fastest way to get from point A to B. Two years after my move, I ditched my car in favor of a second bike, and haven’t looked back since (except to check for traffic).</p>
<p>As a side-note, my biking habits have changed dramatically in just the last few years, with the advent of cheap and widely available e-bike components. I don’t think the extent of the e-bike revolution has fully percolated through our collective consciousness, and Chicago (with its oblong shape and correspondingly lengthy commutes) represents a perfect use case for the benefits of this newly widespread tech.</p>
<p><a href="/assets/images/schelling-out/e-bike.png"><img src="/assets/images/schelling-out/e-bike.png" alt="e-bike.png" title="e-bike" class="center-image" /></a>
<cite>My DIY e-bike, a <a href="https://www.statebicycle.com/collections/core-line/products/wulf-core-line">State Wulf</a>(?) with a <a href="https://www.eco-ebike.com/collections/tongsheng-tsdz2/products/tsdz2-w-850c-torque-sensing-pedal-assist-with-throttle-and-e-brakes-36v-48v-52v-10-18a-250-750w">TSDZ2 mid-drive retrofit kit</a> (running <a href="https://github.com/OpenSourceEBike/TSDZ2-Smart-EBike">open-source firmware</a>!)</cite></p>
<p>So bike-ability is important to me, but how best to approximate it with available data? I considered a few options, including using <a href="https://cityratings.peopleforbikes.org/">existing bike rankings</a>, or even computing a metric for bike lane coverage via something like <a href="https://www.cyclosm.org/">OpenStreetMap</a>. But actual usage data is a better indicator than mere infrastructure, and so I opted to for American Community Survey’s <a href="https://data.census.gov/cedsci/">B08006: SEX OF WORKERS BY MEANS OF TRANSPORTATION TO WORK</a> table (collated by Place), which includes a estimate of bicycle commuters. Dividing this by the total population (S0101: AGE AND SEX) gives the result per 100K:</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code> biking
City State
Boulder Colorado 4911.883946
Cambridge Massachusetts 4077.337000
Berkeley California 3330.760167
Portland Oregon 3017.927981
Fort Collins Colorado 2665.157254
Eugene Oregon 2607.109124
Madison Wisconsin 2329.253849
Washington District of Columbia 2280.996162
San Francisco California 2140.528405
Chico California 1930.423680
</code></pre></div></div>
<p>One thing to note here and elsewhere: college towns (with their manicured campuses and perennially youthful populations) score well on some metrics in a way that may skew overall results. For instance, if a disproportionate number of city bicycle lanes are on-campus (which could be the case in e.g. <a href="https://www.google.com/maps/@42.3732168,-71.1202181,16z/data=!5m1!1e3">Cambridge, Massachusetts</a>), a high score may not actually be representative of the “adult” lived experience.</p>
<h2 id="vegan-friendliness">Vegan-friendliness</h2>
<p>As a third factor, I wanted to find a metric for the type of community I’m looking to join/build in my future wintering destination. After considering a few options (e.g. existing EA and adjacent communities), and not finding enough data on smaller cities, I ended up choosing vegan-friendliness (and specifically the number of vegan restaurants per-capita) as a reasonable proxy.</p>
<p>On its surface this is a bit of an odd fit because, despite having access to some <a href="https://www.kalishvegan.com/">excellent</a> <a href="https://www.kalemyname.com/">nearby</a> <a href="https://www.urbanveganthai.com/">vegan</a> <a href="https://www.samandgerties.com/">restaurants</a>, I don’t eat out very often. And even though I think nearly everyone can (and <em>should</em>) be plant-based (see <a href="/blog/against-thanksgiving">my 2018 post</a> on the topic), I don’t have any hard restrictions when it comes to being friends, roommates, romantic partners, etc. with non-vegans. But it’s still a potential source of social friction, and knowing someone has gone vegan makes it much more likely that we’ll have other areas of political and philosophical overlap.</p>
<p>Getting the data on vegan restaurants was initially a bit of a pain, since I wrongly expected <a href="https://www.happycow.net/">Happy Cow</a> (the vegan equivalent of Yelp) to be easily scrape-able. After toying with the idea of leveraging Mechanical Turk for the task, I eventually learned that Yelp itself has a <a href="https://www.yelp.com/developers">public API</a> with an <em>excellent</em> free tier. A few GraphQL queries later, and we get the following top 10 list of vegan restaurants per 100K:</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code> vegan
City State
Portland Oregon 18.084208
Berkeley California 16.891756
Hollywood Florida 15.026100
Burbank California 13.974678
Salt Lake City Utah 9.513176
Honolulu Hawaii 9.117744
Costa Mesa California 8.935113
Atlanta Georgia 8.822674
Inglewood California 8.351738
Oakland California 8.169823
</code></pre></div></div>
<p>There’s still a bit of a California slant here, but also a few predictable (Portland) and surprising (Atlanta) entries from other states.</p>
<h2 id="housing-costs">Housing costs</h2>
<p>It’s all fine and good to calculate the <em>positive</em> qualities of potential destination cities, but “Econ 101” gives us reason to suspect that nicer things generally cost more <a href="https://en.wikipedia.org/wiki/Citation_needed">[citation needed]</a>. And being notoriously cheap (for a <a href="/blog/personal-giving">good cause</a>), it’s easier for me to imagine being happy getting a “good deal” on a less desirable location than the opposite.</p>
<p>I initially toyed with the idea of using existing cost-of-living metrics, but opted against these sources for a few reasons. First, as I mentioned above, unless you’re crunching the numbers yourself, it’s hard to find a dataset that includes the smallish cities on my list. And even if you could, the existing data I found was not entirely applicable: most sources seem to adjust for earnings potential, but as a remote consultant, my income is location-independent.</p>
<p>So instead I combined my need for a metric with my fledgling desire to become a real estate baron, and simply looked at housing costs. Zillow kindly provides a bunch of <a href="https://www.zillow.com/research/data/">free data</a> collected from their listings (just don’t use it for <a href="https://www.wsj.com/articles/zillows-shuttered-home-flipping-business-lost-881-million-in-2021-11644529656">real estate speculation</a>!), and so I borrowed their median figures. Here’s the corresponding top 10 list showing the least expensive locations:</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code> housing
City State
Shreveport Louisiana 39439.0
Macon Georgia 47197.0
Jackson Mississippi 48537.0
Peoria Illinois 53939.0
Detroit Michigan 54475.0
Birmingham Alabama 60394.0
Columbus Georgia 64872.0
Dayton Ohio 70471.0
Montgomery Alabama 71741.0
Wichita Falls Texas 73433.0
</code></pre></div></div>
<p>As you can see there’s… not much overlap with any of our “positive qualities” lists above. No matter; we’ll just have to somehow aggregate our four metrics to get a list of candidate cities that perform well across the board.</p>
<h1 id="aggregation">Aggregation</h1>
<p>A problem with combining the raw data from the four factors is that the scales are wildly different. And so we’ll need to normalize each source, while taking care to ensure that the resulting score is still intuitively useful. I opted to do this by first dividing each column by the raw data from the Chicago, Illinois row:</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code> biking housing vegan winter summer
City State
Chicago Illinois 754.626952 297669.0 2.475979 0.05625 0.516984
</code></pre></div></div>
<p>I then took the <code class="language-plaintext highlighter-rouge">log2()</code> of each column, meaning that an additional “point” represents a doubling/halving of the underlying quantity (with Chicago pegged at <code class="language-plaintext highlighter-rouge">0.0</code> on all scales). The intuition behind this is the same as <a href="https://en.wikipedia.org/wiki/Marginal_utility#Law_of_Diminishing_marginal_utility">diminishing marginal utility</a>: if my hometown has one vegan restaurant and gets a second it’s a big event, but when a new one opens in e.g. Portland it’s a drop in the bucket.</p>
<p>Finally, to actually aggregate the data into a single overall <code class="language-plaintext highlighter-rouge">total</code>, I just took the average for the four factors. This is clearly overly simplistic, but does have a few nice properties. First is that, if a hypothetical city scores <code class="language-plaintext highlighter-rouge">1.0</code> on all metrics, we’d only expect it to be <em>twice</em> as good as Chicago overall (i.e. points shouldn’t compound). Another is that, if a different city’s scores are <code class="language-plaintext highlighter-rouge">[1.0, -1.0, 1.0, -1.0]</code>, its <code class="language-plaintext highlighter-rouge">total</code> should be <code class="language-plaintext highlighter-rouge">0.0</code> (a higher-variance equivalent of Chicago).</p>
<h1 id="conclusion">Conclusion</h1>
<p>Finally, we’re ready for the overall results, but I’ll comment on them up here, since the full city list is quite long.</p>
<p>First, does the ordering make any intuitive sense? I think so! Known desirable locations like Berkeley and Portland do really well, and suspected undesirable locations like Newark and Fargo fare poorly.</p>
<p>Is it <em>actionable</em> though? I’m not sure! I suspect that it under-weights costs relative to my preferences. I already knew that Berkeley is great, and have been continuously deciding not to move there for nearly a decade, mostly because it’s expensive. This could be corrected by e.g. adjusting the aggregation weights a bit, but I’ll leave that as an exercise for the reader.</p>
<p>Any surprises? Yes! <strong>Gainesville ends up looking like a potential Schelling point</strong> (but note the aforementioned college town caveat), and I’m already considering an exploratory trip. Even though it’s the country’s most stereotypical wintering location, Florida still looks to be somewhat underrated, at least in terms benefits per cost.</p>
<p>Anything depressing predictable? Unfortunately! As expected, the housing market is already pretty efficient at pricing in desirable qualities:</p>
<p><a href="/assets/images/schelling-out/plot.png"><img src="/assets/images/schelling-out/plot.png" alt="plot.png" title="plot" class="center-image" /></a></p>
<p>In case you’re wondering, the cities on the Pareto frontier of the above plot are (from left to right):</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code> housing mean(biking + vegan + winter)
City State
Berkeley California -2.166879 2.894951
Portland Oregon -0.720326 2.114104
Tempe Arizona -0.184675 1.999728
New Orleans Louisiana 0.053609 1.795931
Gainesville Florida 0.854543 1.760714
Tallahassee Florida 1.061017 0.864989
Columbia South Carolina 1.268380 0.799584
McAllen Texas 1.306605 0.651316
Edinburg Texas 1.570956 0.474594
Birmingham Alabama 2.301232 -0.049612
Macon Georgia 2.656942 -0.458740
Shreveport Louisiana 2.916014 -0.787479
</code></pre></div></div>
<p>Any other thoughts? Sure! <strong>Cold winters suck</strong>, so let’s all meet somewhere warm for a few months next year and see how it goes.</p>
<h2 id="results">Results</h2>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code> biking housing vegan winter total
City State
Berkeley California 2.142016 -2.166879 2.770248 3.772590 1.303595
Gainesville Florida 1.117079 0.854543 0.780399 3.384664 1.227337
Tempe Arizona 1.329176 -0.184675 1.161235 3.508773 1.162902
Portland Oregon 1.999723 -0.720326 2.868659 1.473931 1.124397
New Orleans Louisiana 0.739080 0.053609 1.451345 3.197368 1.088280
Hollywood Florida -0.717470 -0.222100 2.601399 3.434937 1.019353
Boulder Colorado 2.702441 -1.017822 0.899562 2.222392 0.961315
Cambridge Massachusetts 2.433792 -1.559463 1.255650 2.648835 0.955763
Orlando Florida -1.171810 0.527952 1.594634 3.601198 0.910395
St. Petersburg Florida -0.573223 0.002294 1.410371 3.478732 0.863635
Washington District of Columbia 1.595828 -1.084329 1.521066 2.086415 0.823796
Oakland California 0.687321 -1.462576 1.722306 3.147990 0.819008
Honolulu Hawaii 0.229185 -1.088344 1.880678 3.039528 0.812209
Tampa Florida -0.657579 0.051231 1.139613 3.459432 0.798539
Costa Mesa California -0.198188 -1.428551 1.851487 3.758182 0.796586
Savannah Georgia -0.255070 0.678344 0.128550 3.421701 0.794705
Charleston South Carolina 0.403625 -0.202618 0.426785 3.311202 0.787799
Chico California 1.355082 -0.191401 0.255839 2.502500 0.784404
Atlanta Georgia -0.419401 -0.436258 1.833217 2.926937 0.780899
College Station Texas 0.482567 0.732154 -0.577164 3.237579 0.775027
San Francisco California 1.504131 -2.313744 1.422406 3.181750 0.758909
West Palm Beach Florida -0.932180 0.205645 1.045346 3.462666 0.756295
Davie Florida -1.678146 0.393529 1.612148 3.410019 0.747510
Columbia South Carolina -1.581587 1.268380 0.563633 3.416706 0.733427
Tallahassee Florida -0.922398 1.061017 0.041832 3.475533 0.731197
Fort Lauderdale Florida -0.962683 -0.497783 1.522490 3.564032 0.725211
Tucson Arizona 0.390097 0.272473 -0.578040 3.426679 0.702242
Sacramento California 0.116168 -0.527433 1.157821 2.715432 0.692398
Seattle Washington 1.327673 -1.402490 1.236571 2.293145 0.690980
Austin Texas -0.177935 -0.924690 1.207541 3.346651 0.690313
Richmond Virginia 0.517944 0.352283 1.096752 1.448053 0.683006
Eugene Oregon 1.788615 -0.325378 0.193002 1.758182 0.682884
Pompano Beach Florida -1.261591 0.049339 1.112872 3.488286 0.677781
Burbank California -1.096257 -1.653189 2.496744 3.527429 0.654945
McAllen Texas -2.082474 1.306605 0.768940 3.267480 0.652110
Wilmington North Carolina -1.344590 0.384498 0.806648 3.296775 0.628666
Concord California -0.926712 -0.498019 1.365348 3.161987 0.620521
Santa Clara California 0.353730 -1.812287 1.147196 3.344899 0.606708
Edinburg Texas -2.117161 1.570956 0.273462 3.267480 0.598947
Philadelphia Pennsylvania 0.269809 0.269624 0.303291 2.098942 0.588333
Inglewood California -1.393411 -1.154314 1.754077 3.688056 0.578882
Fort Collins Colorado 1.820385 -0.527097 -0.071921 1.643144 0.572902
Irvine California -0.304730 -1.437896 0.977508 3.545846 0.556146
Boston Massachusetts 0.656954 -1.287168 0.743086 2.648835 0.552341
Long Beach California -1.109554 -1.157459 1.423572 3.556482 0.542608
Huntington Beach California -0.525972 -1.327111 1.023257 3.511899 0.536415
Baton Rouge Louisiana -1.254039 1.105407 -0.493676 3.305808 0.532700
Sunnyvale California 0.189570 -2.083455 1.052259 3.436583 0.518991
San Diego California -0.787561 -1.271233 0.985638 3.641717 0.513712
Pasadena California 0.119192 -1.528185 1.389968 2.581991 0.512593
Durham North Carolina -1.467649 0.066828 0.889061 3.058894 0.509427
Norman Oklahoma -0.087962 1.151239 -0.664436 2.139930 0.507754
Asheville North Carolina -1.073773 -0.113853 0.772256 2.945552 0.506037
Los Angeles California -0.919500 -1.417222 1.121308 3.638860 0.484689
Scottsdale Arizona -1.036645 -0.738044 0.590733 3.581991 0.479607
Pittsburgh Pennsylvania 0.138689 0.767195 0.677784 0.779091 0.472552
Baltimore Maryland -0.930871 0.593406 0.999800 1.620152 0.456497
Athens Georgia -0.343891 0.557919 -0.656402 2.662965 0.444118
Denton Texas -1.287415 -0.002774 0.792887 2.696324 0.439804
Salt Lake City Utah 0.620573 -0.730072 1.941928 0.332575 0.433001
Birmingham Alabama -2.235491 2.301232 -0.728315 2.814968 0.430479
Ann Arbor Michigan 1.251728 0.042455 1.190750 -0.339850 0.429016
Clearwater Florida -1.076466 0.167151 -0.538104 3.530515 0.416619
Coral Springs Florida -2.963827 0.763381 0.850494 3.415037 0.413017
New Haven Connecticut 0.622749 0.489883 0.591448 0.360590 0.412934
Miami Florida -1.055553 -0.494680 -0.130904 3.465894 0.356951
Santa Ana California -1.062230 -0.957692 0.058676 3.718142 0.351379
Jacksonville Florida -1.778604 0.716357 -0.555336 3.357118 0.347907
Orange California -1.903617 -1.133613 1.014846 3.718142 0.339152
Jersey City New Jersey -0.942944 -0.995080 1.465744 2.143966 0.334337
Garden Grove California -1.817342 -0.969648 0.910020 3.511899 0.326986
Modesto California -1.400753 -0.137729 -0.113467 3.239466 0.317503
Norfolk Virginia -1.424107 0.586606 1.025971 1.374396 0.312573
Palm Bay Florida -1.080163 0.683348 -1.568145 3.500928 0.307193
Las Vegas Nevada -2.990623 0.093010 1.219099 3.054613 0.275220
Albuquerque New Mexico -0.693051 0.385454 -0.345692 2.004446 0.270231
Anaheim California -1.787178 -1.069736 0.897797 3.300395 0.268255
Houston Texas -1.882922 0.733733 -0.880235 3.334342 0.260984
Chattanooga Tennessee -2.238647 0.492615 0.420185 2.620152 0.258861
Macon Georgia -3.983572 2.656942 -0.376977 2.984331 0.256145
Chesapeake Virginia -3.472157 0.517334 1.373412 2.792007 0.242119
Carlsbad California -1.952946 -1.423428 0.815485 3.764749 0.240772
Santa Fe New Mexico -1.105789 -0.610279 1.492895 1.415037 0.238373
Downey California -2.237860 -1.065512 0.820409 3.556482 0.214704
Pomona California -1.291166 -0.708111 -0.324381 3.386368 0.212542
Mesa Arizona -1.274058 -0.087379 -0.834806 3.231903 0.207132
Fullerton California -1.646548 -1.147992 0.169774 3.660150 0.207077
Columbus Georgia -2.066752 2.198041 -2.357086 3.237579 0.202356
Glendale California -1.914729 -1.509920 0.887081 3.527429 0.197972
Bakersfield California -2.730272 0.433107 0.139025 3.145979 0.197568
Jackson Mississippi -3.896322 2.616552 -0.928126 3.159996 0.190420
Denver Colorado 0.612942 -0.804137 0.437967 0.682518 0.185858
Boise Idaho 0.946649 -0.514036 -0.222925 0.715432 0.185024
Santa Rosa California -0.643119 -0.953659 -0.555943 3.065291 0.182514
Virginia Beach Virginia -1.581353 0.436724 -0.186042 2.230006 0.179867
St. Louis Missouri -0.786591 0.831099 -0.900530 1.736966 0.176189
Reno Nevada -0.922409 -0.417641 0.460486 1.742299 0.172547
Clovis California -2.139177 -0.229617 0.012439 3.137908 0.156311
Salem Oregon -0.209830 -0.164851 -0.534796 1.676958 0.153496
Chandler Arizona -1.314484 -0.439685 -0.450672 2.968489 0.152730
Sandy Springs Georgia -2.918166 -0.052725 0.901829 2.830075 0.152203
Roanoke Virginia -2.261495 1.021981 0.276805 1.671377 0.141734
Roseville California -2.814082 -0.728780 0.935973 3.312995 0.141221
Temecula California -3.567511 -0.794051 1.554458 3.459432 0.130466
Fresno California -2.412054 0.285923 -0.424649 3.137908 0.117426
Augusta Georgia -2.564448 1.454645 -1.322933 3.017702 0.116993
Providence Rhode Island -0.877858 -0.032492 0.758927 0.715432 0.112802
Shreveport Louisiana -3.050156 2.916014 -2.215605 2.903324 0.110715
North Las Vegas Nevada -3.439314 -0.015230 0.884497 3.115477 0.109086
Provo Utah 0.604372 -0.269708 -1.511664 1.699069 0.104414
San Jose California -1.225528 -1.585143 0.158452 3.145979 0.098752
Pasadena Texas -1.883377 0.876247 -1.911596 3.389771 0.094209
Richardson Texas -2.696993 0.265249 0.020327 2.869603 0.091637
Riverside California -1.590180 -0.699944 -0.641414 3.344899 0.082672
Cape Coral Florida -3.188209 0.135017 0.057753 3.406664 0.082245
Hialeah Florida -2.556545 0.432047 -0.880785 3.403301 0.079604
Visalia California -2.432972 0.379370 -0.807618 3.230006 0.073757
San Mateo California -0.284479 -2.199505 -0.387442 3.214739 0.068663
Brownsville Texas -3.296097 1.389010 -1.209015 3.433289 0.063437
Torrance California -1.880537 -1.404593 0.135527 3.457812 0.061642
Ontario California -2.188497 -0.719594 -0.117537 3.311202 0.057115
Tacoma Washington -1.509216 -0.451054 0.143755 2.065291 0.049755
Cary North Carolina -2.466404 -0.132583 0.694303 2.139930 0.047049
High Point North Carolina -2.947338 1.621182 -0.497780 2.035189 0.042251
Rancho Cucamonga California -1.974131 -0.655039 -0.525875 3.311202 0.031231
El Paso Texas -3.507914 1.151681 -0.749090 3.129792 0.004894
Raleigh North Carolina -2.321711 0.016848 0.166965 2.139930 0.000406
Chicago Illinois 0.000000 -0.000000 0.000000 0.000000 0.000000
Pembroke Pines Florida -4.075066 0.418627 0.238432 3.410019 -0.001598
San Antonio Texas -2.929160 0.690643 -0.980676 3.199309 -0.003977
Cincinnati Ohio -2.171605 0.748550 0.232840 1.135883 -0.010866
Gilbert Arizona -2.161182 -0.514663 -0.407863 2.968489 -0.023044
Norwalk California -1.448528 -0.979440 -1.347461 3.660150 -0.023056
Fremont California -2.319555 -1.365222 0.072171 3.436583 -0.035205
Santa Clarita California -2.989000 -0.727647 0.306070 3.121629 -0.057790
Elizabeth New Jersey -1.949665 -0.043716 -0.180347 1.869603 -0.060825
Hayward California -1.936966 -1.244636 -0.427502 3.250737 -0.071673
Grand Rapids Michigan -0.533040 0.499135 0.699834 -1.054448 -0.077704
El Cajon California -3.196357 -0.693656 0.189976 3.311202 -0.077767
Waco Texas -2.554370 1.165976 -1.777739 2.773892 -0.078448
Nashville Tennessee -2.625033 -0.168392 0.491524 1.864721 -0.087436
Murrieta California -2.818751 -0.626664 -0.457896 3.459432 -0.088776
Lakewood Colorado -1.036303 -0.403443 0.050603 0.936274 -0.090574
Dallas Texas -3.045150 0.300784 -0.620973 2.886562 -0.095755
Newport News Virginia -1.622903 0.994432 -2.205216 2.307608 -0.105216
Escondido California -1.769008 -0.875757 -0.902911 3.019900 -0.105555
Memphis Tennessee -2.913350 1.856832 -1.970442 2.493040 -0.106784
Glendale Arizona -1.421741 0.216996 -2.620229 3.280370 -0.108921
San Bernardino California -3.258026 -0.312627 -0.459215 3.467505 -0.112472
El Monte California -1.452982 -0.909911 -1.438271 3.228106 -0.114612
Richmond California -1.230187 -1.193071 -1.527685 3.365782 -0.117032
Plano Texas -3.051121 -0.198400 -0.236497 2.869603 -0.123283
Lexington Kentucky -1.625524 0.756397 -0.412649 0.637430 -0.128869
Huntsville Alabama -3.320097 0.693400 -0.412376 2.353637 -0.137087
Irving Texas -3.270918 0.334751 -0.346065 2.590887 -0.138269
Louisville Kentucky -2.218831 0.670100 -0.648379 1.505640 -0.138294
Columbus Ohio -1.702103 0.818907 -0.027677 0.103093 -0.161556
League City Texas -3.065776 0.241047 -1.501985 3.493040 -0.166735
Vallejo California -2.979921 -0.670259 -0.642453 3.364054 -0.185716
Phoenix Arizona -1.430518 -0.111500 -2.730357 3.343145 -0.185846
Miami Gardens Florida -5.006708 0.129689 0.533147 3.403301 -0.188114
Oklahoma City Oklahoma -3.016488 1.326170 -1.075768 1.784271 -0.196363
Colorado Springs Colorado -1.580701 -0.238272 -0.108476 0.926937 -0.200102
Little Rock Arkansas -3.170569 1.011489 -1.326569 2.408343 -0.215461
Bellevue Washington -1.280472 -1.222507 -0.910684 2.314786 -0.219775
Cleveland Ohio -1.526667 1.670162 -0.883792 -0.385654 -0.225190
Murfreesboro Tennessee -1.892575 0.133884 -1.919351 2.548893 -0.225830
Tulsa Oklahoma -2.738674 1.279809 -1.769409 2.098942 -0.225867
Oceanside California -2.888869 -0.858195 -1.107650 3.703177 -0.230307
Arlington Texas -3.613098 0.552414 -0.965240 2.867164 -0.231752
Lancaster California -3.677616 -0.043397 -0.103068 2.637430 -0.237330
Centennial Colorado -1.714475 -0.635164 -0.424604 1.584963 -0.237856
Indianapolis Indiana -1.993118 1.034386 -0.873015 0.579013 -0.250547
Midland Texas -3.174656 0.739686 -1.714253 2.886562 -0.252532
Antioch California -3.177091 -0.058280 -1.513279 3.480329 -0.253664
Chula Vista California -3.049402 -0.902141 -0.769984 3.420038 -0.260298
Elk Grove California -2.869658 -0.648388 -0.539628 2.715432 -0.268448
Garland Texas -3.306552 0.528007 -1.606763 3.017702 -0.273521
Detroit Michigan -1.682213 2.450043 0.103397 -2.252387 -0.276232
Killeen Texas -3.864005 1.346818 -1.922426 3.048167 -0.278289
Westminster Colorado -1.247392 -0.498242 -1.526061 1.849975 -0.284344
Charlotte North Carolina -3.612402 0.170659 -0.436588 2.431639 -0.289338
Edison New Jersey -2.773973 -0.223740 0.171446 1.325486 -0.300156
Fort Worth Texas -3.168199 0.560468 -1.507931 2.551934 -0.312746
Omaha Nebraska -2.409727 0.733637 -1.267179 1.374396 -0.313775
Henderson Nevada -3.208685 -0.337998 -0.975256 2.908078 -0.322772
Simi Valley California -2.623598 -0.872812 -1.645493 3.524336 -0.323513
West Covina California -2.758425 -0.848004 -1.438943 3.386368 -0.331801
New York New York -0.291996 -1.338828 0.455884 -0.506960 -0.336380
Peoria Illinois -2.365068 2.464308 -1.486236 -0.295456 -0.336490
Mobile Alabama -4.398111 1.529021 -2.211354 3.372677 -0.341553
Everett Washington -1.685731 -0.627501 -0.453729 1.043854 -0.344621
Winston–Salem North Carolina -2.567010 1.203109 -2.627299 2.260063 -0.346227
Rio Rancho New Mexico -3.155081 0.575203 -1.365221 2.187627 -0.351494
Rochester Minnesota -0.689748 0.391691 -1.587708 0.052467 -0.366660
Alexandria Virginia -0.156274 -0.559591 -1.981257 0.840059 -0.371413
Peoria Arizona -2.594750 -0.382511 -2.241459 3.343145 -0.375115
Victorville California -3.129865 0.012598 -1.738927 2.968489 -0.377541
Olathe Kansas -2.847120 0.507275 -0.806659 1.199309 -0.389439
Kansas City Missouri -3.204755 1.123361 -0.193652 0.260063 -0.402997
Dayton Ohio -2.510456 2.078607 -1.768941 0.183712 -0.403416
Frisco Texas -5.665964 -0.512569 1.273296 2.869603 -0.407127
Sparks Nevada -2.069411 -0.292993 -1.424963 1.742299 -0.409014
Thousand Oaks California -3.041426 -0.887301 -1.652441 3.533594 -0.409515
Spokane Washington -1.612284 -0.071748 0.496722 -0.880418 -0.413546
Hartford Connecticut -2.173498 1.041302 -1.583650 0.602665 -0.422636
Lincoln Nebraska -0.492628 0.534241 -1.849425 -0.317482 -0.425059
Buffalo New York -1.042759 0.820627 -1.199932 -0.816288 -0.447670
Minneapolis Minnesota 1.289658 0.025243 0.172781 -3.754888 -0.453441
Ventura California -3.014507 -1.120766 -1.455475 3.318361 -0.454477
Milwaukee Wisconsin -1.295490 0.978530 -0.515197 -1.481869 -0.462805
Aurora Colorado -2.857760 -0.334724 -0.672613 1.548893 -0.463241
Kansas City Kansas -2.934879 1.304472 -0.955148 0.260063 -0.465098
Lansing Michigan -1.040589 1.619274 -0.479770 -2.432959 -0.466809
Greensboro North Carolina -2.582098 1.086706 -2.888314 2.035189 -0.469703
Salinas California -3.936015 -0.909299 -0.432698 2.903324 -0.474938
Syracuse New York -1.188475 1.239381 -0.879628 -1.695994 -0.504943
Cedar Rapids Iowa -1.441020 1.155098 -0.769633 -1.481869 -0.507485
Wichita Kansas -1.868761 1.477582 -3.299070 1.135883 -0.510873
Davenport Iowa -2.535718 1.341613 -1.332659 -0.036069 -0.512567
Stockton California -2.458920 -0.100752 -2.989691 2.872038 -0.535465
Des Moines Iowa -2.195016 1.002401 -0.406506 -1.210567 -0.561938
Arvada Colorado -3.317610 -0.733929 -0.623009 1.849975 -0.564915
Fort Wayne Indiana -1.947713 1.371172 -1.122952 -1.130397 -0.565978
Daly City California -2.431066 -1.899643 -1.377028 2.862274 -0.569092
Vancouver Washington -1.796828 -0.347369 -2.240930 1.473931 -0.582239
Meridian Idaho -2.053093 -0.530561 -1.542317 1.183712 -0.588452
Saint Paul Minnesota -0.454895 0.249745 -1.362394 -1.385654 -0.590639
Amarillo Texas -3.969056 1.581666 -2.310831 1.654503 -0.608744
Stamford Connecticut -1.944410 -0.536136 -0.745973 0.135883 -0.618127
Palmdale California -3.488912 -0.270733 -2.068859 2.731612 -0.619378
Lowell Massachusetts -2.410435 -0.003737 -0.516566 -0.273761 -0.640900
Anchorage Alaska -0.454084 0.321517 -1.850242 -1.339850 -0.664532
Dearborn Michigan -2.568487 0.987593 -0.445188 -1.481869 -0.701590
Sugar Land Texas -6.805274 0.307642 -0.458897 3.369234 -0.717459
Moreno Valley California -4.299475 -0.450072 -2.368974 3.197368 -0.784230
Madison Wisconsin 1.626032 0.151094 -0.418175 -5.339850 -0.796180
Grand Prairie Texas -5.707094 0.661151 -2.279589 3.137908 -0.837525
Bridgeport Connecticut -4.367892 0.364224 -0.879958 0.693573 -0.838010
Akron Ohio -3.707669 1.873323 -2.237555 -0.252387 -0.864858
Corona California -5.916725 -0.794766 -0.960013 3.300395 -0.874222
Overland Park Kansas -3.665807 0.380486 -2.287937 1.199309 -0.874790
Miramar Florida -8.049974 -0.045436 0.262026 3.403301 -0.886017
Allentown Pennsylvania -3.478934 0.581978 -1.639647 0.086415 -0.890038
Manchester New Hampshire -2.244972 0.059415 -1.517690 -0.754888 -0.891627
Springfield Massachusetts -3.309156 0.468587 -0.948889 -0.695994 -0.897090
Toledo Ohio -2.218760 1.950067 -2.745605 -1.754888 -0.953837
Allen Texas -5.916318 -0.287822 -1.373254 2.760812 -0.963316
Naperville Illinois -3.168425 0.298994 -1.888531 -0.073063 -0.966205
Elgin Illinois -2.564046 0.485045 -1.507084 -1.295456 -0.976308
Green Bay Wisconsin -1.698421 1.049224 -1.410926 -2.880418 -0.988108
Sterling Heights Michigan -4.105408 0.670941 -1.733953 0.199309 -0.993822
Yonkers New York -2.672196 -0.875530 -2.389128 0.789433 -1.029484
Joliet Illinois -4.503893 0.858193 -1.896439 0.346651 -1.039098
Newark New Jersey -4.425534 -0.021967 -0.362496 -0.432959 -1.048591
Warren Michigan -2.983875 1.483316 -1.787095 -2.252387 -1.108008
Paterson New Jersey -5.641049 0.003542 -0.983653 0.467505 -1.230731
Worcester Massachusetts -3.191207 0.010531 -0.769304 -2.639410 -1.317878
Sioux Falls South Dakota -2.093099 0.468681 -2.252985 -3.339850 -1.443451
Independence Missouri -8.787841 1.192736 -1.606787 1.230006 -1.594377
Fargo North Dakota -1.195830 0.738456 -1.641309 -6.339850 -1.687706
</code></pre></div></div>
<p>See <a href="https://github.com/danwahl/schelling-out">here</a> for the source code and data used to generate everything above.</p>Dan WahlWhere should we all go next winter?Thing Obscurer2022-05-13T00:00:00+00:002022-05-13T00:00:00+00:00https://danwahl.net/blog/thing-obscurer<p>XKCD author Randall Munroe’s <a href="https://xkcd.com/thing-explainer/">Thing Explainer</a> used only the most common <a href="/assets/js/thing-obscurer/words.js">ten hundred words</a> to simply explain complicated topics. <a href="https://slatestarcodex.com/2014/05/24/nydwracus-fnords/">Nydwracu’s Fnords</a> did nearly the opposite, removing everything non-essential and clearly exposing the underlying message.</p>
<p>I like both of these ideas, so let’s combine them. Copy/paste some text into the box, and below you’ll see the same text with the most common “ten hundred” words (and some punctuation) removed.</p>
<!--more-->
<ul id="markdown-toc">
<li><a href="#input-text" id="markdown-toc-input-text">Input text</a></li>
<li><a href="#result" id="markdown-toc-result">Result</a></li>
<li><a href="#why-do-this" id="markdown-toc-why-do-this">Why do this?</a></li>
<li><a href="#improvements" id="markdown-toc-improvements">Improvements</a></li>
</ul>
<h1 id="input-text">Input text</h1>
<script src="/assets/js/thing-obscurer/words.js"></script>
<script language="javascript">
var words;
// parse words from | separated string to array
words = __WORDS.split("|");
// define function
function obscure() {
// read text in editor
var text = document.getElementById("editor").value;
// replace newlines with space
text = text.replace(new RegExp("\n", "g"), " ");
// remove all punctuation
text = text.replace(new RegExp("[.,\/#!$%\^&\*;:{}=_`~()]", "g"), "");
// remove all words in text that are also in words
for (var i = 0; i < words.length; i++) {
text = text.replace(new RegExp("\\b" + words[i] + "\\b", "gi"), "");
}
// write text to output box
document.getElementById("output").innerText = text;
}
// run obscure after page is entirely loaded
window.onload = obscure;
</script>
<div style="display: block;">
<textarea id="editor" name="editor" style="width: 100%; height: 300px;" onchange="obscure()">
Four score and seven years ago our fathers brought forth on this continent, a new nation, conceived in Liberty, and dedicated to the proposition that all men are created equal.
Now we are engaged in a great civil war, testing whether that nation, or any nation so conceived and so dedicated, can long endure. We are met on a great battle-field of that war. We have come to dedicate a portion of that field, as a final resting place for those who here gave their lives that that nation might live. It is altogether fitting and proper that we should do this.
But, in a larger sense, we can not dedicate—we can not consecrate—we can not hallow—this ground. The brave men, living and dead, who struggled here, have consecrated it, far above our poor power to add or detract. The world will little note, nor long remember what we say here, but it can never forget what they did here. It is for us the living, rather, to be dedicated here to the unfinished work which they who fought here have thus far so nobly advanced. It is rather for us to be here dedicated to the great task remaining before us—that from these honored dead we take increased devotion to that cause for which they gave the last full measure of devotion—that we here highly resolve that these dead shall not have died in vain—that this nation, under God, shall have a new birth of freedom—and that government of the people, by the people, for the people, shall not perish from the earth.
</textarea>
</div>
<h1 id="result">Result</h1>
<div id="output" style="padding: 20px; background-color: #f5f5f5;">
</div>
<h1 id="why-do-this">Why do this?</h1>
<p>After taking a lengthy break from social media sites like Twitter and mainstream news outlets like The New York Times, I’m finding that I no longer have the stomach for their brand of emotionally/politically charged language (if I ever did). And this is not to mention their less sophisticated cousins like TikTok and CNN.</p>
<p>My (literal) mental model is that of a machine, pretty often functional, but sometimes on the fritz (in which state it’s worse than useless). There’s a large control panel with a bunch of knobs and dials, and through a non-trivial amount of effort, I’ve found a combination of settings that makes everything run smoothly enough.</p>
<p><a href="/assets/images/thing-obscurer/joy.jpg"><img src="/assets/images/thing-obscurer/joy.jpg" alt="joy.jpg" title="joy" class="center-image" /></a></p>
<p>But when I read an article that uses charged language, it’s the metaphorical equivalent of someone reaching in and haphazardly mashing a bunch of knobs and dials.</p>
<p><a href="/assets/images/thing-obscurer/anger.jpg"><img src="/assets/images/thing-obscurer/anger.jpg" alt="anger.jpg" title="anger" class="center-image" /></a></p>
<p>This is not good! And it can sometimes take me a while to reach a new equilibrium again.</p>
<p>As an example, take this recent <a href="https://www.politico.com/news/magazine/2022/05/12/carrick-flynn-save-world-congress-00031959">Politico piece</a> on Oregon Congressional candidate Carrick Flynn, which was my proximal motivation to write this post. The Thing Obscurer analysis of the opening section looks like:</p>
<blockquote>
<p>Flynn contend deadly robots type boogeyman donor Sam Bankman-Fried eccentric 30– cryptocurrency billionaire chief donor PAC 10 million Flynn’s campaign Bankman-Fried’s support Flynn baffled observers energized Flynn’s opponents cast billionaire’s intervention shady carpetbagging crypto-baron influence cryptocurrency regulation Washington February rival campaign manager denounced Bankman-Fried “ tax-dodging billionaire”</p>
</blockquote>
<p>There’s nothing <em>wrong</em> with this per-se, in the sense that the author is simply reporting on what Flynn and his opponents have said. But it’s also akin to <a href="https://en.wikipedia.org/wiki/Fear,_uncertainty,_and_doubt">FUD</a>, via the uncritical use/repetition of charged language like <code class="language-plaintext highlighter-rouge">[deadly, boogeyman, baffled, shady, carbetbagging, baron, denounced, tax-dodging]</code>. Words like this are meant to emotionally <em>engage</em> (and maybe even <em>entertain</em>), but not necessarily <em>inform</em> the reader. And when it comes to news, I mostly just want to be informed.</p>
<p><strong>Edit 2022-11-21: This example obviously did not age well, but the general point remains (I think).</strong></p>
<h1 id="improvements">Improvements</h1>
<p>Thing Obscurer obviously doesn’t work very well in its current form, but I do think something <em>like</em> it might, ironically, help clarify tone/intent, and allow people like me to identify (and selectively avoid) needlessly inflammatory authors and sources. Some thoughts on potential improvements/alternatives:</p>
<ul>
<li>As it stands, Thing Obscurer does not conform to the “iron law” of ~13% summary length suggested by the SSC post. This could be achieved by reducing the XKCD word list a bit, but doing so would probably make the results even less comprehensible.</li>
<li>There are already <a href="https://openai.com/blog/learning-to-summarize-with-human-feedback/">ML summary algorithms</a> that work reasonably well. Something similar could probably be trained to distill articles into Fnords, or even directly remove/reword inflammatory language.</li>
<li>However, sometimes emotional content is essential to the message, and the act of removing/rewording may risk discarding the core content. But even when this is true, our capacity for emotional engagement is not infinite, and we should at least choose our battles wisely.</li>
</ul>Dan WahlComplicated Stuff Without Simple Words.LEEP Cost-Effectiveness Analysis2021-01-24T00:00:00+00:002021-01-24T00:00:00+00:00https://danwahl.net/blog/leep-cea<p>A silver lining for me in the otherwise dark cloud of 2020 appeared in mid-August, when Charity Entrepreneurship (CE) announced that <a href="https://forum.effectivealtruism.org/posts/Cbm2hu4RFfCqSHKcr/2020-top-charity-ideas-charity-entrepreneurship#Lead_paint_regulation">lead paint regulation</a> was among its “top charity ideas” for the year. More good news followed a few months later with the <a href="https://forum.effectivealtruism.org/posts/fd96FtLFACeAshqJP/introducing-leep-lead-exposure-elimination-project">official launch of the Lead Exposure Elimination Project</a> (LEEP), a new (CE-incubated) charity dedicated to reducing the global burden of lead poisoning.</p>
<p>As someone who has <a href="/blog/lead-hypothesis">previously written on the prospect of lead as an EA cause area</a>, I was thrilled to see this new interest, and my excitement was undiminished after meeting LEEP co-founders <a href="https://leadelimination.org/about/">Lucia and Jack</a>. In the months since their launch the duo has hit the ground running in Malawi, performing independent testing to confirm the (<em>alarmingly</em> high) lead content in local paint, and cultivating key government contacts to help initiate the regulation process. And all that during a global pandemic, on a shoestring budget, and from halfway across the world!</p>
<p>As part of a review of their <a href="https://docs.google.com/spreadsheets/d/1rlQXHfI9jZW8OtvQX1iJxDKBqmgGvYNRGktf4aHHgFg/edit?usp=sharing">cost-effectiveness analysis</a> (CEA), they asked me to look over their existing calculations (which were derived from a <a href="https://docs.google.com/spreadsheets/d/15em-6D42SIxnR91fpG7w9mHLQKtS5WiQC0dTTnpt4B4/edit#gid=1890023799">more complex model developed by CE</a> during their evaluation process). After a quick sanity check of the overall structure, I decided to focus on one particular area of the analysis, taking a deep dive into the section on health outcomes.</p>
<p>The full model used to generate the following plots is available as a Jupyter notebook <a href="https://github.com/ordinaryevidence/leep-cea">here</a>.</p>
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<ul id="markdown-toc">
<li><a href="#population" id="markdown-toc-population">Population</a></li>
<li><a href="#health-outcomes" id="markdown-toc-health-outcomes">Health outcomes</a></li>
<li><a href="#creating-cohorts" id="markdown-toc-creating-cohorts">Creating cohorts</a></li>
<li><a href="#results" id="markdown-toc-results">Results</a></li>
<li><a href="#conclusion" id="markdown-toc-conclusion">Conclusion</a></li>
</ul>
<h1 id="population">Population</h1>
<p>Any policy change in Malawi is still potentially years away, and because of the <a href="https://www.unicef.org/reports/toxic-truth-childrens-exposure-to-lead-pollution-2020">health dynamics of lead poisoning</a>, even a successful intervention will mostly benefit future humans. And so it’s crucial for LEEP to have a good understanding of the changing population in any target country. While projecting even a few months into the future can be a <a href="https://www.metaculus.com/questions/4617/will-2020-be-the-warmest-year-on-record/">nontrivial task</a>, the UN maintains <a href="https://population.un.org/wpp/Download/Standard/CSV/">country-specific population forecasts</a> through 2100(!?).</p>
<p>Even for a relatively deterministic metric like population growth, these estimates strike me as <em>wildly</em> optimistic (Toby Ord, for instance, <a href="https://www.newyorker.com/culture/annals-of-inquiry/how-close-is-humanity-to-the-edge">thinks there’s a one in six chance</a> that the overall human population in 2100 will be 0). However, worlds where the population deviates significantly from the UN’s projection are also probably ones where lead poisoning is, for whatever reason, no longer among our biggest problems. (Depending on your philosophical commitments, this may imply that you should prioritize work on existential risks like engineered pathogens and misaligned AI, but I’m ignoring this consideration for the purposes of the following analysis.) Regardless, I’ll use the median UN estimates as a “best guess” estimate for the future population in LEEP’s target country (here and below assumed to be Malawi).</p>
<p><a href="/assets/images/leep-cea/population.png"><img src="/assets/images/leep-cea/population.png" alt="population.png" title="population" class="center-image" /></a></p>
<p><cite>In this and most of the following graphs, historical data is shown with “dashed” lines, and projected data with “solid” lines.</cite></p>
<p>Given the conservative assumption that the intervention only affects future generations, population growth turns out to be a critical factor when projecting health outcomes. It may, for instance, be preferable to target a smaller country with faster population growth rather than a larger one with slower growth, especially when the effects of LEEP’s intervention will only be felt decades from now. As we’ll see below, certain assumptions about the interaction between population and paint market growth could make this estimate doubly important.</p>
<p>Also, breaking up the estimate into five-year age groups will allow us to account for age-specific disease burden (i.e. intellectual impairment for young childrean, heart disease for older adults).</p>
<h1 id="health-outcomes">Health outcomes</h1>
<p>The second data set used in my analysis comes from IMHE’s <a href="http://ghdx.healthdata.org/gbd-results-tool">Global Burden of Disease</a> (GBD) tool. This mind-bogglingly expansive resource accounts for the negative health outcomes of hundreds of diseases, injuries, and risk factors across 200+ countries and over several decades. For the purposes of this analysis, I downloaded the the <a href="http://ghdx.healthdata.org/gbd-results-tool?params=gbd-api-2019-permalink/31dd9609036871b0e51adfd08e154284">full data set</a> associated with the “lead exposure” risk factor, divided by country and age group.</p>
<p>The GBD data includes absurdly precise estimates, reporting that, for instance, the total number of DALYs experienced by 60-64 year olds in Malawi due to rheumatic heart disease in 1991 was 10.156989. How they arrived at this particular and extremely specific number is probably very interesting, but for the purposes of this analysis, I’ll simply take their “middle” estimate as the given for each category. From 2000 to 2019, the totally DALY estimates for each five-year age group are as follows:</p>
<p><a href="/assets/images/leep-cea/dalys-by-age.png"><img src="/assets/images/leep-cea/dalys-by-age.png" alt="dalys-by-age.png" title="dalys-by-age" class="center-image" /></a></p>
<p>Although these numbers look relatively flat, the story is actually somewhat more optimistic when combined with Malawi’s population growth over the same time period. Dividing the total DALYs by the UN population numbers for each year, we can come up with a DALYs/100k estimate for each year and age group.</p>
<p><a href="/assets/images/leep-cea/dalys-per-100k.png"><img src="/assets/images/leep-cea/dalys-per-100k.png" alt="dalys-per-100k.png" title="dalys-per-100k" class="center-image" /></a></p>
<p>In the above graph, the dashed line represents the previous ~20 years of “known” DALY rates among the different age groups of Malawi’s population due to lead exposure. Based on the observation that these numbers appear to be decreasing (perhaps due to the lingering effects of the <a href="https://en.wikipedia.org/wiki/Tetraethyllead#Phaseout_and_ban">global ban on lead in gasoline</a>?), and the conservative assumption that they will continue to decrease on their own (without actually reaching zero), I fit an exponential function to the DALY rates in each age group and projected through 2100.</p>
<p>Using these projected DALY/100k rates, I then multiplied by the estimated future population to find the projected total burden of lead exposure in Malawi, by age group, through 2100:</p>
<p><a href="/assets/images/leep-cea/dallys-by-age-2100.png"><img src="/assets/images/leep-cea/dallys-by-age-2100.png" alt="dallys-by-age-2100.png" title="dallys-by-age-2100" class="center-image" /></a></p>
<p>One potentially counterintuitive observation is already apparent: following <a href="https://www.un.org/en/sections/issues-depth/ageing/">historical trends in developing countries</a>, a larger percentage of Malawians will reach old age, and the percentage of total lead burden affecting the elderly will increase accordingly (as shown in the the darker blue lines above).</p>
<h1 id="creating-cohorts">Creating cohorts</h1>
<p>This brings us to the trickiest part of the analysis, and the one in which I have the most personal uncertainty. So far we’ve calculated the expected total burden of lead poisoning in Malawi through 2100. But the critical question remains: <em>if</em> LEEP is successful, and helps introduce lead paint regulations, <em>what percentage of the total future lead burden will they have (counterfactually) helped to avert?</em></p>
<p>Because young children, with growing bodies and brains, are the most susceptible to the pernicious effects of lead exposure, any attempt to answer the above likely requires us to consider LEEP’s impact by birth cohort. Sorting in this way allows us to conservatively count only the DALYs associated with children who successfully avoid lead exposure in childhood as a result of LEEP’s intervention. And so I started by creating yearly cohorts using the total projected DALYs by age, shown below in five year increments.</p>
<p><a href="/assets/images/leep-cea/dalys-by-cohort.png"><img src="/assets/images/leep-cea/dalys-by-cohort.png" alt="dalys-by-cohort.png" title="dalys-by-cohort" class="center-image" /></a></p>
<p><cite>To help read this graph, try tracing the yellow line of children born in 2025 as they age throughout the remainder of the century.</cite></p>
<p>But how should we determine whether or not children born after the lead paint ban will actually avoid contact with lead paint? Viewed in some ways, this seems like a depressingly intractable problem. The exact answer could, for instance, depend on how often typical Malawians repaint their house, how often families with young children move to new houses, the pace of new home construction relative to the retirement of existing housing stock, and other hard-to-measure factors.</p>
<p>For the purposes of this analysis, I made the following assumptions:</p>
<ul>
<li>The expected total burden in the future population assumes the current baseline level of lead exposure.</li>
<li>As population increases, with no intervention (from LEEP or otherwise), <em>the rate of exposure will remain constant</em>, even as the population grows.</li>
<li>However, <em>after</em> lead paint regulation is introduced, no <em>additional</em> lead paint will enter the housing market.</li>
<li>Any population growth after paint regulation will cause a corresponding increase in “unleaded” paint share on the housing market.</li>
<li>Therefore, the amount of lead paint burden averted will be proportional to the future population relative to the population when the regulation was introduced.</li>
</ul>
<p>To give a (hopefully) intuitive example: imagine a country with a current population of 100 people, and a corresponding total lead burden of 10 DALYs/year. If the population were to <em>triple</em> over a given period <em>without</em> a prior lead paint ban, then we could naively expect the total burden at the end of that period to be 30 DALYs/year, matching the increase in population.</p>
<p>However, if a lead paint regulation was introduced prior to that period of growth, then while the population would still increase to 300 people, the lead burden would remain fixed at 10 DALYs/year. Although all the new housing stock would be lead-free, the 100 people living in existing homes would still experience the original burden. As such, this intervention could claim credit for a counterfactual (30 - 10 =) 20 DALYs averted per year, or about 67% of the “expected” burden.</p>
<p>Using Malawi’s population growth, and under the most optimistic projection that LEEP successfully helps introduce paint regulations <em>this year</em>, the largest possible fraction of avertable DALYs that they can claim credit for is represented by the yellow “Growth” line on the chart below:</p>
<p><a href="/assets/images/leep-cea/factors.png"><img src="/assets/images/leep-cea/factors.png" alt="factors.png" title="factors" class="center-image" /></a></p>
<p>The other factors shown on the graph above are Discount, which visualizes the effect of the (apparently standard?) 4% <a href="https://en.wikipedia.org/wiki/Discount_rate">discount rate</a> (quantifying the intuition that, for various reasons, we care more about near-term benefits than long-term ones), and Source, which corrects for the fact that not all lead exposure comes from paint (the specific 25% number is a conservative assumption based on CE’s understanding of expert consensus).</p>
<p>And so, the total factor by which any given cohort’s DALYs are avertable is equal to the product of these three factors, with Growth locked <em>at their birth year</em> (capturing the conservative assumption that the effects of lead are an immutable function of adolescent exposure). We can use this product to adjust the original cohorts graph as shown below:</p>
<p><a href="/assets/images/leep-cea/avertable-dalys-by-cohort.png"><img src="/assets/images/leep-cea/avertable-dalys-by-cohort.png" alt="avertable-dalys-by-cohort.png" title="avertable-dalys-by-cohort" class="center-image" /></a></p>
<h1 id="results">Results</h1>
<p><strong>Edit 2021-06-03: In retrospect, I think I may be comparing apples and oranges with the CE/LEEP CEA comparisons in the graph below, due to varying model assumptions. I may come back to fix this later, but it doesn’t change my overall conclusions re: LEEP’s cost effectiveness.</strong></p>
<p><em>Finally</em>, we’re in a position to calculate the health benefits of a successful LEEP intervention. To do this, we create a customized reduction factor, as described above, based on the estimated year of the intervention, multiply it by the total avertable DALYs by birth cohort, and sum for each year across living cohorts. Critically, both CE and LEEP also assume that, even without their intervention, lead paint would eventually be regulated by Malawi at some point in the near future (perhaps with the help of a similar organization like <a href="https://ipen.org/projects/eliminating-lead-paint">IPEN</a>). In the graph shown below, I’ve calculated the expected DALYs averted per year for both LEEP and the counterfactual, keeping the CE/LEEP assumption that the intervention “moves up” the regulation timeline by eight years from 2033 to 2025:</p>
<p><a href="/assets/images/leep-cea/intervention-vs-counterfactual.png"><img src="/assets/images/leep-cea/intervention-vs-counterfactual.png" alt="intervention-vs-counterfactual.png" title="intervention-vs-counterfactual" class="center-image" /></a></p>
<p>The total DALYs averted by the LEEP’s hypothetical 2025 success can be calculated by simply adding up the year-by-year difference between the Intervention and Counterfactual curves, and is plotted in dark blue on the graph below:</p>
<p><a href="/assets/images/leep-cea/intervention-minus-counterfactual.png"><img src="/assets/images/leep-cea/intervention-minus-counterfactual.png" alt="intervention-minus-counterfactual.png" title="intervention-minus-counterfactual" class="center-image" /></a></p>
<p>I’ve also included the CE and LEEP estimates on this graph. Overall, the final estimates are quite similar (i.e. well within a factor of two), which lends personal credence to the remainder of the CE/LEEP analyses.</p>
<h1 id="conclusion">Conclusion</h1>
<p>In this post I took a deep dive into a small subsection of a CEA on the regulation of lead paint in Malawi, but the full CE/LEEP analyses don’t stop there. In addition to health outcomes, they both consider other detrimental effects of lead exposure on future earnings potential, as well as the costs (and likelihood of success) of any potential intervention. While I haven’t done an in-depth analysis on these other factors, I don’t see anything so far that would lead me to believe that they are systematically biased towards favorable assumptions.</p>
<p>I think I may have unearthed at least one interesting idea during my review, which is the importance of <em>growth</em> as a differentiating factor in country selection. Because both overall DALYs <em>and</em> the avertable fraction are dependent on population (in my model at least), countries with larger projected growth could be especially good choices. (Eg. a country with low population growth also builds fewer new houses, and therefore will take the longest to refresh their existing housing stock, leading to more future childhood lead exposure).</p>
<p>This was also a lot of fun, and I even learned a bit about a few useful data sets, as well as some interesting CEA “best practices.” In the future, I may consider updating this model in various ways, perhaps by including the additional factors from the CE/LEEP analyses, or converting the point estimates to probability distributions as part of a <a href="https://en.wikipedia.org/wiki/Monte_Carlo_method">Monte Carlo</a> approach.</p>
<p>As I reach this final paragraph, I now realize that I’ve probably buried the lede, which is that <em>CE and LEEP’s analysis both show that lead paint regulation is a promising intervention, with conservative estimates that place it in the neighborhood of top GiveWell recommended charities</em>. If you take one thing away from this post, it should be that lead poisoning remains a <em>bad thing</em>, and organizations like LEEP provide a potentially <em>very</em> cost-effective way to allocate your charitable dollars.</p>Dan WahlProjecting the positive potential of a possible Pb paint prohibition.SSC Podcast (and Unsong Audiobook)2020-05-24T00:00:00+00:002020-05-24T00:00:00+00:00https://danwahl.net/blog/ssc-podcast<p><strong>Edit 2021-01-21: Scott Alexander just launched his new blog <a href="https://astralcodexten.substack.com/">Astral Codex Ten</a>! See <a href="/blog/ssc-podcast#ac10-podcast">below</a> for info on the corresponding podcast.</strong></p>
<p>I’ve been a fan of Scott Alexander’s <a href="https://slatestarcodex.com/">Slate Star Codex</a> (SSC) for several years, and have already written <a href="/blog/slate-star-codex">more</a> <a href="/blog/lightfoot-prospiracy">than</a> <a href="/blog/chicago-budget">a few</a> posts referencing his work. Other readers have been <a href="https://guzey.com/favorite/slate-star-codex/">similarly</a> <a href="https://www.lesswrong.com/posts/vwqLfDfsHmiavFAGP/the-library-of-scott-alexandria">inspired</a>, and a few have even taken the time to produce <a href="https://sscpodcast.libsyn.com/">audio versions</a> of his more recent entries. As an avid podcast listener, I’ve found these to be an extremely useful way to keep up-to-date with SSC in the context of my regular media workflow.</p>
<p>Unfortunately, audio coverage for older posts is somewhat sparse, and the same is true of his (<a href="https://www.scottaaronson.com/blog/?p=3259">wonderful</a>) serial novel <a href="http://unsongbook.com/">Unsong</a>, with one <a href="https://unsong.libsyn.com/">ongoing effort</a> currently only about halfway finished. That’s why, late last year, I started a project to convert his entire back catalog into audio using Amazon Polly.</p>
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<ul id="markdown-toc">
<li><a href="#ssc-podcast" id="markdown-toc-ssc-podcast">SSC Podcast</a></li>
<li><a href="#unsong-audiobook" id="markdown-toc-unsong-audiobook">Unsong Audiobook</a></li>
<li><a href="#ac10-podcast" id="markdown-toc-ac10-podcast">AC10 Podcast</a></li>
</ul>
<h1 id="ssc-podcast">SSC Podcast</h1>
<p><a href="/ssc-podcast/"><img src="/ssc-podcast/img/ssc-podcast.png" alt="ssc-podcast" title="ssc-podcast" class="center-image" /></a></p>
<p><cite>Just some quick artwork I made for the podcast feed. For an an actual SSC ebook, see <a href="https://github.com/georgjaehnig/webpages-to-ebook#examples">here</a>.</cite></p>
<p>Having previously worked with text-to-speech (TTS) software to create a <a href="/blog/speaking-aid">web app for conversing while I had mono</a>, I was surprised by just how much the technology has improved in recent years. After testing the available options, I decided to use <a href="https://aws.amazon.com/polly/">Amazon Polly</a>, both because of its generous free tier, and also the <a href="https://docs.aws.amazon.com/polly/latest/dg/NTTS-main.html">Neural TTS engine</a>, which produces incredibly lifelike cadence and intonation.</p>
<p>Polly has several limitations, however. The first is that the API can only process several thousand characters at a time, meaning the user is responsible for chunking the raw text, and concatenating the resulting audio files. Also, although the <a href="https://aws.amazon.com/polly/pricing/?nc=sn&loc=4">1 million character monthly limit</a> <em>sounds</em> like a lot, that didn’t stop me from running up a $250 AWS bill on my first attempt…</p>
<p>But, after some scraping and fine tuning, I eventually got a workable prototype and processed the entire corpus of SSC posts to date. The GitHub repo, which contains the scripts, audio files, and Jekyll-based web interface used to generate the podcast feed, can be found <a href="https://github.com/danwahl/ssc-podcast">here</a>.</p>
<p>I also run a local server that periodically scans the <a href="https://slatestarcodex.com/archives/">SSC Archives</a> and automatically generates new episodes as posts are published. To subscribe, simply add the following link to your podcast app:</p>
<p><a href="https://danwahl.net/ssc-podcast/feed.xml">https://danwahl.net/ssc-podcast/feed.xml</a></p>
<p>Note: there are still a few bugs that cause occasional issues, mostly related to novel (or sometimes broken) HTML formatting in Scott’s posts. I plan to go through and fix this at some point, but PRs are also welcome!</p>
<h1 id="unsong-audiobook">Unsong Audiobook</h1>
<p><a href="/unsong-audiobook/"><img src="/unsong-audiobook/img/unsong-audiobook.png" alt="unsong-audiobook" title="unsong-audiobook" class="center-image" /></a></p>
<p><cite>Just some quick artwork I made for the audiobook feed. For an an actual Unsong ebook, see <a href="https://github.com/moorederodeo/Unsong-In-Ebook-Format/releases/">here</a>.</cite></p>
<p>Using a fork of the SSC Podcast repo (with a few minor modifications), I also generated an audiobook version of Unsong. Although there are some predictable issues with the Names of God, and Uriel occasionally speaks in acronyms, I think it turned out nicely overall. The GitHub repo is <a href="https://github.com/danwahl/unsong-audiobook">here</a>, and you can subscribe on your podcast app using the following link:</p>
<p><a href="https://danwahl.net/unsong-audiobook/feed.xml">https://danwahl.net/unsong-audiobook/feed.xml</a></p>
<h1 id="ac10-podcast">AC10 Podcast</h1>
<p><a href="/ac10-podcast/"><img src="/ac10-podcast/img/ac10-podcast.png" alt="ac10-podcast" title="ac10-podcast" class="center-image" /></a></p>
<p><cite>Just some quick artwork I made for the podcast feed.</cite></p>
<p>After a <em>long</em> absence, Scott Alexander has finally returned with his new blog, <a href="https://astralcodexten.substack.com/">Astral Codex Ten</a>. Between this and the Biden inauguration, I am having a <strong>good week</strong>. And so it is with pleasure that I present the corresponding <a href="https://danwahl.net/ac10-podcast/">AC10 Podcast</a>.</p>
<p>So far everything seems to be working, but since I had to port the parsing script from Wordpress to Substack html, I expect things to be a little bumpy at first. Please <a href="https://github.com/danwahl/ac10-podcast/issues">create an issue on GitHub</a> if you notice any problems.</p>
<p>Side note: why AC10 and not <a href="https://astralcodexten.substack.com/p/still-alive/comments#comment-1095963">ACT</a> or <a href="https://astralcodexten.substack.com/p/youre-probably-wondering-why-ive/comments#comment-1095780">ACX</a>? I’m not sure about the preferred acronym, but given the digital nature of the narrator, I thought it best to stick to 1s and 0s.</p>
<p><a href="https://danwahl.net/ac10-podcast/feed.xml">https://danwahl.net/ac10-podcast/feed.xml</a></p>Dan WahlAutomated readings of Slate Star Codex and Unsong, using Amazon Polly.Personal Giving2020-02-27T00:00:00+00:002020-02-27T00:00:00+00:00https://danwahl.net/blog/personal-giving<p>I remember the moment I first suspected that Mitt Romney might be a good person. It was during a 2012 presidential debate against Barack Obama, and I was watching from my apartment in just outside of DC. Although my estimation of Romney has <a href="https://www.politico.com/news/2020/02/05/mitt-romney-impeachment-vote-speech-transcript-110849">improved considerably</a> in recent months, at the time he was still the <a href="https://en.wikipedia.org/wiki/Mitt_Romney_2012_presidential_campaign#47%_comment">47%</a>, <a href="https://en.wikipedia.org/wiki/Binders_full_of_women">binders-full-of-women</a>, <a href="https://en.wikipedia.org/wiki/Mitt_Romney_2012_presidential_campaign#Dog_incident">strap-the-dog-to-the-car-roof</a> candidate, and I despised everything he stood for (in a way that seems heartbreakingly naive post-2016). And so, watching the debate that night, I was surprised to learn that Romney donated 10% of his yearly income to charity, a substantial sum given his vast wealth.</p>
<p>Granted, his donations were made in the form of a <a href="https://swampland.time.com/2012/01/24/tax-returns-and-tithing-how-mitt-romney-gives-away-16-of-his-income/">tithe to the Mormon Church</a>, rendering them <a href="https://southpark.cc.com/full-episodes/s07e12-all-about-mormons">potentially useless</a> (<a href="https://en.wikipedia.org/wiki/The_Church_of_Jesus_Christ_of_Latter-day_Saints#Controversy_and_criticism">or worse</a>). But a thought still lingered: if this otherwise awful guy was so generous with <em>his</em> money, what did that say about <em>me</em>, who had never given any of mine? By the end of 2012 I had vowed to at least match Mitt, and to donate 10% of my income to worthwhile charities.</p>
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<ul id="markdown-toc">
<li><a href="#giving-history-cont" id="markdown-toc-giving-history-cont">Giving History, cont.</a> <ul>
<li><a href="#creating-a-culture-of-giving" id="markdown-toc-creating-a-culture-of-giving">Creating a Culture of Giving</a></li>
</ul>
</li>
<li><a href="#the-giving-process" id="markdown-toc-the-giving-process">The Giving Process</a> <ul>
<li><a href="#donor-advised-funds" id="markdown-toc-donor-advised-funds">Donor-Advised Funds</a></li>
<li><a href="#effective-altruism-funds" id="markdown-toc-effective-altruism-funds">Effective Altruism Funds</a></li>
</ul>
</li>
<li><a href="#my-giving" id="markdown-toc-my-giving">My Giving</a></li>
<li><a href="#epilogue" id="markdown-toc-epilogue">Epilogue</a></li>
</ul>
<h1 id="giving-history-cont">Giving History, cont.</h1>
<p>Although I suspected that many organizations were better than the LDS Church, popular evaluators like <a href="https://www.charitynavigator.org/">Charity Navigator</a> and <a href="https://www.charitywatch.org/">CharityWatch</a> left me dissatisfied. As a spreadsheet fanatic, I was accustomed to doing thorough, quantitative research before any big expenditure. But besides <a href="https://www.redcross.org/content/dam/redcross/about-us/publications/FY-2018-Form-990.pdf">sprawling financial disclosure forms</a>, there was precious little information to be found about nonprofit quality. Why, I wondered, was more collective effort being spent <a href="https://www.consumerreports.org/laptop-computers/best-laptops-of-the-year/">vetting thousand dollar laptops</a> than billion dollar charitable interventions?</p>
<p>Thankfully, I was far from the only one thinking along these lines. I soon stumbled across <a href="https://www.givewell.org/">GiveWell</a>, <a href="https://www.givewell.org/about/story">founded by two former hedge fund analysts</a> who had asked themselves the same question several years earlier (and, unlike me, had actually done something about it). From there I discovered the broader <a href="https://www.effectivealtruism.org/">Effective Altruism</a> movement, including the works of contemporary utilitarian philosopher <a href="https://petersinger.info/">Peter Singer</a>.</p>
<h2 id="creating-a-culture-of-giving">Creating a Culture of Giving</h2>
<p>It was Singer’s book <a href="https://www.thelifeyoucansave.org/the-book/">“The Life You Can Save”</a> that prompted me to write this particular post. In chapter 5, “Creating a Culture of Giving,” Singer advocates for breaking the (<a href="https://www.biblegateway.com/passage/?search=Matthew+6%3A1-4&version=NIV">biblically inspired?</a>) taboo against public giving, writing:</p>
<blockquote>
<p>If our sense of fairness makes us less likely to give when others are not doing so, the converse also holds: we are much more likely to do the right thing if we think others are already doing it. More specifically, we tend to do what others in our “reference group”— those with whom we identify—are doing. And studies show that the amount people give to charity is related to how much they believe others are giving.</p>
</blockquote>
<p>This ethos <a href="https://forum.effectivealtruism.org/posts/5d3td2YpuCiE8L7yr/to-inspire-people-to-give-be-public-about-your-giving">has been adopted by the Effective Altruism movement</a> as a tool for both public outreach and <a href="https://www.givingwhatwecan.org/pledge/">internal</a> <a href="https://www.thelifeyoucansave.org/take-the-pledge/">accountability</a>. And while (having been raised Catholic) I still feel somewhat uncomfortable sharing my giving history, the good examples of people like <a href="http://peterhurford.com/other/donations.html">Peter Huford</a> and <a href="https://www.aaronhamlin.com/giving">Aaron Hamlin</a> have persuaded me that it’s a worthwhile exercise.</p>
<h1 id="the-giving-process">The Giving Process</h1>
<p>But before sharing specifics, a quick word on two relatively new financial tools have made my giving life significantly more efficient: Donor-Advised and Effective Altruism Funds.</p>
<h2 id="donor-advised-funds">Donor-Advised Funds</h2>
<p><a href="https://en.wikipedia.org/wiki/Donor-advised_fund">Donor-Advised Funds</a> (DAFs) are a unique type of investment vehicle that allow individuals to donate money <em>now</em> and decide where to allocate it <em>later</em>. Although they’ve recently received some bad press for their (inevitable) use as <a href="https://www.vox.com/recode/2019/12/18/21010108/larry-page-philanthropy-foundation-donor-advised-fund-christmas">tax shelters for the ultra-wealthy</a>, they remain an indispensable tool for the average charitable taxpayer. By separating the donation/allocation steps of the giving process, they allow donors to give at the most financially opportune moment, and <em>then</em> undertake any charitable research at their leisure, all while earning interest on their pending gifts.</p>
<p>DAFs also facilitate the donation of non-cash assets, such as long-term appreciated stocks, cryptocurrency, and even real estate, which individual charities might have difficulty accepting directly. And <a href="https://www.fidelitycharitable.org/">Fidelity’s DAF</a> has an additional feature, <a href="https://www.fidelitycharitable.org/giving-account/gift4giving.html">Gift4Giving</a>, which lets donors convert a small portion of their giving account into a kind of charitable gift card, allowing the recipient to allocate the money to their preferred charity. In the spirit of propagating Singer’s “Culture of Giving” (and because I’m a reluctant shopper), I started using Gift4Giving as my default Christmas present several years ago, and chronicled my family’s (surprisingly positive) response in a <a href="/family-giving">previous post</a>.</p>
<p>DAFs have some downsides, too. Investment options are typically limited to large ETFs, but with much higher fees, and all allocations must nominally be approved by the financial institution (hence the “advised”). For more on how best to utilize DAFs for efficient giving, see Brian Tomasik’s <a href="https://reducing-suffering.org/advanced-tips-on-personal-finance/">Advanced Tips on Personal Finance</a> and Ben Khun’s <a href="https://www.benkuhn.net/giving-101">Giving money away: a guide</a>.</p>
<p>Another obvious problem with DAFs is that you still (eventually) have to figure out where to donate the money. Organizations like <a href="https://www.givewell.org/charities/top-charities">GiveWell</a> and <a href="https://animalcharityevaluators.org/donation-advice/recommended-charities/">Animal Charity Evaluators</a> make that process somewhat easier by providing a shortlist of thoroughly vetted charities every year, but this still requires some non-trivial effort to keep up-to-date with the latest analyses, and to weigh their recommendations against your personal values (see <a href="/ace-vs-givewell">this post</a> for a particularly ill-conceived attempt).</p>
<h2 id="effective-altruism-funds">Effective Altruism Funds</h2>
<p>Thankfully, the <a href="https://app.effectivealtruism.org/funds">Effective Altruism Funds</a> (EAFs) help bridge that last gap. Rather than make recommendations at the organization level, EAFs allow donors to divide their donation between the four primary EA cause areas: <a href="https://app.effectivealtruism.org/funds/global-development0">Global Health and Development</a>, <a href="https://app.effectivealtruism.org/funds/animal-welfare">Animal Welfare</a>, the <a href="https://app.effectivealtruism.org/funds/far-future">Long-term Future</a>, and <a href="https://app.effectivealtruism.org/funds/ea-community">EA Meta</a>. From there, the donations are allocated to charities (and sometimes individuals) by mission-aligned, subject-level experts.</p>
<p>While EAFs are great for people with established trust in the EA community, I’d still encourage everyone first considering charitable giving to begin by conducting their own research. The official <a href="https://www.effectivealtruism.org/articles/introduction-to-effective-altruism/">Introduction to Effective Altruism</a> is a great place to start.</p>
<h1 id="my-giving">My Giving</h1>
<p>The document displayed below contains a continuously updating record of my personal giving, from a compulsive, pre-Romney donation to NPR in 2011 through the present day.</p>
<iframe width="100%" height="600" src="https://docs.google.com/spreadsheets/d/e/2PACX-1vTb21bp3mWFiWo3KQgGVpEVgP5UdZDdvFhQHHbYlEcD1qnTVK1DNJGGHMvTroZ6Wdh4EjQRGMGYM6Ai/pubhtml?widget=true&headers=false"></iframe>
<p>(Note that this only includes allocated gifts, not total DAF donations. The large spike(s) in 2020 coincided with my decision that EAFs were preferable to paying large fees storing money indefinitely in my DAF.)</p>
<h1 id="epilogue">Epilogue</h1>
<p>I’ve lived an <em>extremely fortunate</em> life so far, and thanks to a timely intervention by a former GOP presidential candidate, I’ve had the opportunity to share a small portion of that good fortune with others. As of writing this post, in the eight years since I began giving to charity, I’ve donated about a third of my pre-tax income, mostly to effective organizations. I plan to continue giving at least that much for as long as I’m able.</p>
<p>Lest that seem overly burdensome, during that same time period I’ve also gone on yearly vacations, purchased a condo in Chicago, maxed out my 401k contributions, and eaten out at countless (expensive vegan) restaurants. If you’re anything like me, a large portion of your income is <a href="https://www.economicshelp.org/blog/12309/concepts/diminishing-marginal-utility-of-income-and-wealth/">probably superfluous</a>, and would be better utilized by <a href="https://www.givedirectly.org/">those in need</a>.</p>
<p>In fact, I haven’t done half as much as I could (and given the amount of suffering in the world, <em>should</em>) have. But it’s a start.</p>
<p>Your move, Mitt.</p>Dan WahlOn my quest to become a better person than Mitt Romney.The Single Flip Problem2019-12-27T00:00:00+00:002019-12-27T00:00:00+00:00https://danwahl.net/blog/single-flip-problem<p>While on vacation in a mysterious city, you take a wrong turn down a dark alley and are approached by two unusually generous street hustlers.</p>
<p>The first offers to play a simple coin game. After you wager any sum of money \(M\), he’ll flip a fair coin. If it comes up \(heads\), he’ll pay you \(M\), but if it’s \(tails\), you’ll owe him \(\frac{M}{2}\).</p>
<p>Before you have a chance to accept, the second hustler proposes a slight variation on the same game. You’ll still wager \(M\), but instead he’ll flip a coin for <em>each dollar</em> of your bet. Every time it lands on heads, you win \(\$1.00\), but each tails will cost you \(\$0.50\).</p>
<p>Which game should you play?</p>
<!--more-->
<ul id="markdown-toc">
<li><a href="#expected-value" id="markdown-toc-expected-value">Expected value</a></li>
<li><a href="#variance-and-downside-risk" id="markdown-toc-variance-and-downside-risk">Variance and downside risk</a></li>
<li><a href="#real-world-examples" id="markdown-toc-real-world-examples">Real-world examples</a> <ul>
<li><a href="#finance" id="markdown-toc-finance">Finance</a></li>
<li><a href="#effective-altruism" id="markdown-toc-effective-altruism">Effective Altruism</a></li>
<li><a href="#hockey-and-upside-risk" id="markdown-toc-hockey-and-upside-risk">Hockey and upside risk</a></li>
</ul>
</li>
<li><a href="#conclusion" id="markdown-toc-conclusion">Conclusion</a></li>
</ul>
<h1 id="expected-value">Expected value</h1>
<p>At first glance these games might look identical, and from the perspective of expected value, they are. The payout for the first game (\(G_1\)) is calculated by:</p>
\[E_1=P_{heads}\times{M} - P_{tails}\times\frac{M}{2}\]
<p>Assuming a fair coin, and a \(\$10\) wager, you have an equal chance of winning \(\$10\) or losing \(\$5\), for an average payout of \(\$2.50\). Pretty good odds! And although the second game (\(G_2\)) takes a little longer to play, it’s clear that the expected value is the same:</p>
\[E_2=\sum_{n=1}^{M} (P_{heads}\times{\$1.00} - P_{tails}\times{\$0.50})\]
<p>For each flip of the coin, you have an equal chance of winning \(\$1.00\) or losing \(\$0.50\), for an average of \(\$0.25\). For a \(\$10\) wager, with 10 corresponding flips, you’d still expect to win \(\$2.50\).</p>
<p>But despite the identical expected payout, there is a good reason to prefer one game over the other.</p>
<h1 id="variance-and-downside-risk">Variance and downside risk</h1>
<p>Imagine you choose to play \(G_1\), and now have to decide how much to wager. You know, from the expected value calculations above, that you’ll get a \(25\%\) payout, on average. Clearly you should wager more than \(\$10\), but is there any limit to how much you should be willing to bet?</p>
<p>Perhaps you’ve already identified a problem. While you have a \(50\%\) chance of <em>doubling</em> your money, you also have a \(50\%\) chance of <em>losing half of it</em>; a risky proposition! In order to visualize this risk, we can plot the probability and payoff associated with each possible outcome:</p>
<p><a href="/assets/images/single-flip-problem/game1.png"><img src="/assets/images/single-flip-problem/game1.png" alt="game1.png" title="game1" class="center-image" /></a></p>
<p>As the bar chart above shows, with a bet of \(M=\$10\) there are two equal probability outcomes for \(G_1\); one (blue) in which you win \(\$10\), and the other (orange) where you lose \(\$5\). Looking at the outcomes for \(G_2\), however, something is clearly different:</p>
<p><a href="/assets/images/single-flip-problem/game2.png"><img src="/assets/images/single-flip-problem/game2.png" alt="game2.png" title="game2" class="center-image" /></a></p>
<p>Even without analyzing the math, it’s obvious that, while in \(G_1\) \(50\%\) of the outcomes were negative (orange), in \(G_2\) you only expect to actually lose money on a handful of bets (about \(17\%\)).</p>
<p>A more formal name for the bars in orange is <a href="https://en.wikipedia.org/wiki/Downside_risk">downside risk</a>, a term that represents the probability of a negative outcome for a particular strategy. This is closely related to the variance, or spread, of the underlying probability distribution, since larger variance means a longer tail, and therefore more area in the negative region.</p>
<p>In \(G_1\), the probabilities are maximally spread, leading to the outcome with the largest downside risk. With \(G_2\), however, each additional dollar you bet is another coin flip, and the more coins get flipped, the more likely you are to get a result close to the expected value (since each flip is independent of the previous one). You can <a href="https://www.random.org/coins/?num=10&cur=60-usd.0050c">test this yourself</a>!</p>
<h1 id="real-world-examples">Real-world examples</h1>
<p>In summary, picking \(G_2\) has the advantage of significantly reducing the risk relative to \(G_1\) without sacrificing any expected value. But is this relevant outside of hypothetical street games in dark alleys of mysterious cities?</p>
<h2 id="finance">Finance</h2>
<p>The obvious example of downside risk in the real world is in finance, the industry responsible for coining the term. When analyzing a portfolio of stocks, investors often care about more than just the expected return of the individual assets. Young adults, with 30+ year careers ahead of them, can afford to prefer relatively risky investments, since they are playing \(G_2\), and each additional year represents another flip of the coin pushing their returns towards the expected value in the long run. Workers approaching retirement, however, are in \(G_1\), and can’t afford to take risks with their accumulated capital, even at the expense of losing out on higher returns.</p>
<p>One tool for managing portfolio risk is <a href="https://en.wikipedia.org/wiki/Post-modern_portfolio_theory">(Post-)</a><a href="https://en.wikipedia.org/wiki/Modern_portfolio_theory">Modern Portfolio Theory</a>, which considers both the expected return and covariance of each of the individual stocks. Stocks that are highly covariant, such as companies within the same sector, tend to move up and down together as market conditions change. Investing with a portfolio full of highly covariant stocks is a single flip game, similar to \(G_1\) above, and may be just as risky as owning a single stock in that sector.</p>
<p>A better strategy is to create a balanced portfolio, full of stocks with high expected returns but uncorrelated variance. That way if an individual stock or whole sector loses value, it’s unlikely to have a significant impact on overall returns, similar to losing a single flip in \(G_2\).</p>
<h2 id="effective-altruism">Effective Altruism</h2>
<p>The same is likely true of charities, as I’ve talked about in a <a href="/stochastic-altruism">series of</a> <a href="/ace-vs-givewell">previous posts</a> about stochastic altruism. As with stocks, even when trying to maximize expected value (or good deeds per dollar donated, in this case), it can still be beneficial to distribute donations among several charities with uncorrelated variance. A balanced portfolio of charities can provide similar altruistic returns with significantly reduced downside risk.</p>
<p><a href="/ace-vs-givewell#ace-vs-givewell-round-2"><img src="/assets/images/ace-vs-givewell/ace-gw-pf2.png" alt="ACE and GiveWell adjusted portfolios" title="Givewell charity portfolios" class="center-image" /></a></p>
<p>And thinking about Effective Altruism (EA) in the context of \(G_1\) vs. \(G_2\) has other implications as well.</p>
<p>In <a href="/stochastic-altruism-2016#why-i-mostly-believe-in-worms-too-i-guess">another post</a> I summarized <a href="http://blog.givewell.org/2016/12/06/why-i-mostly-believe-in-worms/">David Roodman’s research</a> on the “Worm Wars”, the dramatized name for the debate within medical literature on the efficacy of deworming interventions. Though I won’t completely rehash it here, my (non-expert) understanding is that the argument turns on how much credence should be given to <a href="https://web.archive.org/web/20161125024138/http://emiguel.econ.berkeley.edu/assets/miguel_research/46/_Paper__Worms.pdf">series</a> <a href="http://emiguel.econ.berkeley.edu/assets/miguel_research/64/Worms-at-Work_2016-07-12_FINAL_CLEAN.pdf">of</a> <a href="http://www.cochrane.org/CD000371/INFECTN_deworming-school-children-developing-countries">studies</a> showing that childhood deworming medication leads to an increase in consumption (and presumably well-being) later in life.</p>
<p>A simplified view of the problem might be that <strong>we either live in a world in which deworming works or we do not</strong>. In the former, most children would benefit from deworming medication to some degree, but in the latter, all of our money is wasted and would be better donated elsewhere. This scenario is reminiscent of \(G_1\); a single flip problem with high downside risk.</p>
<p>And not all EA interventions share this particular issue. <a href="https://www.givedirectly.org/">GiveDirectly</a>, for example, distributes money collected from donors to poor families in Kenya and Uganda. After running <a href="https://www.givedirectly.org/research-at-give-directly/">several randomized control trials</a> on this intervention, it’s clear that this is a boon for most recipients, with only a handful reporting any negative outcomes. Generally, interventions like this are more similar to \(G_2\), with a “flip” for each recipient, and are less prone to systemic downside risk.</p>
<h2 id="hockey-and-upside-risk">Hockey and upside risk</h2>
<p>By slightly reframing the concept of risk, we can apply a similar model to decisions made at the end of hockey games. If a team is losing by a narrow margin with only a few minutes remaining, a common strategy is to <a href="https://fivethirtyeight.com/features/nhl-coaches-are-pulling-goalies-earlier-than-ever/">replace the goalie</a> with an <a href="https://en.wikipedia.org/wiki/Extra_attacker">extra attacker</a>. Although this increases the risk of allowing the opponent to score on the now-empty net, it also makes it more likely that them team will score the tying goal. And since the margin of victory is (<a href="https://en.wikipedia.org/wiki/Stanley_Cup_playoffs#Current_format">mostly</a>) irrelevant in hockey standings, it sometimes represents a good tradeoff:</p>
<iframe width="100%" height="400" src="https://www.youtube.com/embed/EaDOg_U53vw" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen=""></iframe>
<p>Whereas we were previously trying to minimize downside risk by choosing \(G_2\) over \(G_1\), in the case of the losing hockey team, it’s advantageous to <em>increase</em> the variance (even at the cost of a larger expected losing margin) in order to capture some “upside” risk. As a demonstration, consider the following graph, which shows (made up but plausible) probability density functions for the expected goal differential per minute, with and without a goalie:</p>
<p><a href="/assets/images/single-flip-problem/hockey1.png"><img src="/assets/images/single-flip-problem/hockey1.png" alt="hockey1.png" title="hockey1" class="center-image" /></a></p>
<p>The two curves above are normal distributions, \(\mathcal{N_1}(0.0,\, 0.0625)\) (orange, with a goalie) and \(\mathcal{N_2}(-0.25,\, 0.25)\) (blue, without). When the goalie is pulled, we expect the losing team to fall even further behind, with the goal differential increasing by \(0.25\) per minute on average. However, with the goalie in the game, it’s extremely unlikely that they will score the tying goal, with that probability represented by the area under the orange curve at \(X \geq 1\). Even though the blue distribution has a lower expected value, it also has more area under this same section of the curve, representing a higher chance of scoring the tying goal. In fact, if this data were accurate, it would predict that the losing team should pull their goalie with four minutes remaining in the game, not far from the <a href="http://people.math.sfu.ca/~tim/papers/goalie.pdf">apparently optimal strategy</a> of approximately three minutes.</p>
<p>And why not play without a goalie for the full sixty minutes? As it turns out, pulling the goalie can be viewed as an attempt to move the endgame from \(G_2\) to \(G_1\) in order to capture the “upside” risk of a low probability outcome. The longer the team plays without a goalie, however, the more minutes they accumulate with the new probability distribution, and the more the game resembles \(G_2\) again, with the accumulated expected value outweighing the uncorrelated variance:</p>
\[m\times{\mathcal{N}(\mu,\, \sigma^{2})} = \mathcal{N}(m\times{\mu},\, m\times{\sigma^{2}})\]
<p>Given the assumptions above, a hockey team playing without a goalie for the whole game could expect to lose by more than 15 goals!</p>
<p><a href="/assets/images/single-flip-problem/hockey2.png"><img src="/assets/images/single-flip-problem/hockey2.png" alt="hockey2.png" title="hockey2" class="center-image" /></a></p>
<h1 id="conclusion">Conclusion</h1>
<p>In writing this post, I was gratified to find that some of my disparate thoughts from the past few years can be derived in terms of the same simple thought experiment. The issues with expected value are not unknown, even <a href="https://blog.givewell.org/2011/08/18/why-we-cant-take-expected-value-estimates-literally-even-when-theyre-unbiased/">within the EA community</a>, but statistics are notoriously counterintuitive, and concepts like uncorrelated variance and downside risk seem correspondingly undervalued in popular discourse.</p>
<p>If you take one thing away from this post, try conceptualizing problems not just by their expected value, but also through the lens of \(G_1\) and \(G_2\) (even when not in dark alleys in mysterious cities). For “games” with similar payouts, prefer those that resemble \(G_2\). When constructing portfolios of things like stocks or charities, be wary of accidentally playing \(G_1\) by selecting assets with highly correlated variance. And if you’re down big with only a few minutes to go, try to change the game back to \(G_1\), especially if you’re playing hockey.</p>
<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS-MML_HTMLorMML" type="text/javascript"></script>Dan WahlDownside risks of expected value.Mistake Theory Socialism2019-11-27T00:00:00+00:002019-11-27T00:00:00+00:00https://danwahl.net/blog/chicago-budget<p>Earlier this year, while campaigning for mayor, Lori Lightfoot was subjected to a series of sharp criticisms from Chicago’s progressive community, mostly targeting her participation in the city’s various police reform initiatives. <a href="/lightfoot-prospiracy">I reviewed a handful of these complaints</a> and found them either lacking in substance or ungenerous in their interpretation of the available facts. At the time I compared these leftist anti-Lightfoot narratives to the right’s penchant for <a href="/lightfoot-prospiracy#prospiracy-theory">conspiratorial thinking</a>, and <a href="/lightfoot-prospiracy#personal-note">fretted about the implications</a> for the 2020 presidential election and beyond.</p>
<p>Since her landslide victory, Mayor Lightfoot has defied easy categorization, taking on Chicago’s deeply entrenched political establishment by <a href="https://news.wttw.com/2019/05/20/lightfoot-signs-executive-order-curbing-aldermanic-prerogative">curbing aldermanic prerogative</a>, but also <a href="https://news.wttw.com/2019/10/31/chicago-teachers-strike-comes-end-classes-resume-friday">battling the Teachers Union</a> (which endorsed her opponent) in contract negotiations that featured an 11-day strike. In an issue <a href="/chicago-lead">dear to my heart</a>, she also <a href="https://news.wttw.com/2019/07/09/lead-concerns-halt-chicago-water-meter-installations">paused water meter installations</a> due to lead poisoning concerns (though no word yet on <a href="https://twitter.com/fairvoteIL/status/1093533431882018816">ranked choice voting</a>).</p>
<p>The latest political skirmish has centered around her <a href="https://www.chicago.gov/content/dam/city/depts/obm/supp_info/2020Budget/2020BudgetOverview.pdf">2020 budget proposal</a>, which <a href="https://news.wttw.com/2019/11/26/chicagoans-avoid-major-property-tax-increase-lightfoot-s-1st-budget-passes">passed city council this week</a> by a 39-11 vote. Despite including some progressive victories such as a $15 minimum wage, the council’s <a href="https://www.bettergov.org/news/what-the-gov-what-does-it-mean-to-have-six-democratic-socialists-on-the-chicago-city-council/">socialist caucus</a> represented six of the 11 dissenting votes, and this was indicative of a larger left-leaning backlash.</p>
<!--more-->
<ul id="markdown-toc">
<li><a href="#some-background" id="markdown-toc-some-background">Some background</a></li>
<li><a href="#the-criticism" id="markdown-toc-the-criticism">The criticism</a> <ul>
<li><a href="#putting-on-a-clinic" id="markdown-toc-putting-on-a-clinic">Putting on a clinic</a></li>
</ul>
</li>
<li><a href="#but-what-about-the-police" id="markdown-toc-but-what-about-the-police">But what about the police?</a></li>
<li><a href="#closing-thoughts" id="markdown-toc-closing-thoughts">Closing thoughts</a></li>
</ul>
<h1 id="some-background">Some background</h1>
<p>Before Rahm Emanuel was elected to his first term as mayor in 2011, Chicago ran <a href="https://web.archive.org/web/20100322220717/http://www.cityofchicago.org/city/en/depts/cdph/supp_info/mental_health_centers.html">12 outpatient public mental health centers</a> spread across the city. By the end of his second year in office, and after an <a href="https://chicago.suntimes.com/city-hall/2019/10/3/20897487/public-health-commissioner-nomination-tabled-mental-health-clinics-lightfoot-council">$8 million</a> cut in annual state funding, <a href="https://www.chicago.gov/city/en/depts/cdph/supp_info/behavioral-health/mental_health_centers.html">only six</a> (below, in blue) remained.</p>
<iframe src="https://www.google.com/maps/d/embed?mid=1OJkvaQsfZF9NpOa6pgccHE4C9v_I53lx" marginwidth="0" marginheight="0" width="100%" height="640" scrolling="no"></iframe>
<p>At the time, Emanuel attempted to sell the consolidation to the public in two ways, <a href="https://www.npr.org/2012/04/27/151546358/closure-of-chicago-mental-health-clinics-looms">claiming that</a> “We’re not pulling back from service. In fact, we’re giving more service to more people and we’re adding a new benefit” while simultaneously touting $3 million in savings. Many patients and mental health advocates, however, weren’t buying it, and the resulting protests culminated in a <a href="https://www.chicagoreporter.com/its-time-to-reopen-chicagos-closed-mental-health-clinics/">brief occupation of the now-shuttered Woodlawn facility</a>.</p>
<p>The closure of these six clinics became a defining part of Emanuel’s mayoral legacy, and the effort to reopen them persists nearly eight years later. And along with several other candidates in the 2019 election, Lightfoot <a href="https://www.austinweeklynews.com/News/Articles/1-21-2019/A-different-kind-of-pledge-of-allegiance-/">made a campaign pledge</a> to “reopen the mental health clinics that Mayor Rahm Emanuel closed in 2011 and invest an additional $25 million of city funds in mental health care.”</p>
<h1 id="the-criticism">The criticism</h1>
<p>My original <a href="/lightfoot-prospiracy">Lightfoot Prospiracy</a> post centered around a Slate Star Codex post about <a href="https://slatestarcodex.com/2019/03/04/prospiracy-theories/">Prospiracy Theories</a>, Scott’s attempt to co-opt the language and iconography of conspiratorial thinking to promote factual claims. This time, however, I’m reminded of his post on <a href="https://slatestarcodex.com/2018/01/24/conflict-vs-mistake/">Conflict Vs. Mistake Theory</a>, and in particular, the section on the nature of conflict theory:</p>
<blockquote>
<p>What would the conflict theorist argument against the Jacobite piece look like? Take a second to actually think about this. Is it similar to what I’m writing right now – an explanation of conflict vs. mistake theory, and a defense of how conflict theory actually describes the world better than mistake theory does?</p>
<p>No. It’s the Baffler’s article saying that <a href="https://thebaffler.com/salvos/master-class-on-the-make-hartman">public choice theory is racist, and if you believe it you’re a white supremacist</a>. If this wasn’t your guess, you still don’t understand that conflict theorists aren’t mistake theorists who just have a different theory about what the mistake is. They’re not going to respond to your criticism by politely explaining why you’re incorrect.</p>
</blockquote>
<p>See, while Lightfoot’s 2020 budget falls short of her campaign pledge to reopen the six shuttered clinics, it does call for an additional $9.3 million in Mental Health Services, with which she <a href="https://www.chicago.gov/content/dam/city/depts/cdph/CDPH/Healthy%20Chicago/HealthyChicago_MayorFactsheet_03.pdf">intends to</a>:</p>
<ul>
<li>Fund 20 public and nonprofit health centers to expand care in high-need neighborhoods, regardless of patients’ ability to pay or insurance status.</li>
<li>Create violence prevention programming to address mental health needs in communities most impacted by violence and poverty.</li>
<li>Invest in crisis prevention and response teams for people who have additional mental health challenges and have trouble accessing brick-and-mortar clinics.</li>
<li>Coordinate the city’s mental health system to ensure every resident can access the care they need, where they need it, including an enhanced 311 helpline.</li>
</ul>
<p><em>What would the conflict theorist argument against Lightfoot’s plan look like? Take a second to actually think about this. Is it similar to what I’m writing now - an explanation of Chicago’s recent history of mental healthcare, and a defense of how the six original clinics would actually provide better services than the 20 proposed by Lightfoot?</em></p>
<p><em>No. It’s <a href="https://twitter.com/micahuetricht/status/1199398518886154240">“rose twitter”</a> saying that Lightfoot <a href="https://twitter.com/ChicagoCityDSA/status/1197192151010103297">doesn’t care about the city’s mental health</a>, and <a href="https://twitter.com/marydifino/status/1197507494077173760">was lying</a> when she claimed she did during the campaign.</em></p>
<p><a href="https://twitter.com/ChicagoCityDSA/status/1197192151010103297"><img src="/assets/images/chicago-budget/ChicagoCityDSA.png" alt="ChicagoCityDSA.png" title="ChicagoCityDSA" class="center-image" /></a></p>
<p>In more official circles, the <a href="https://twitter.com/UWFIllinois/status/1199414893759336454">progressive criticism</a> of Lightfoot’s budget has been <a href="https://40thward.org/40th-ward/1069/">less needlessly inflammatory</a>, but <a href="https://twitter.com/CDRosa/status/1199414144241479680">still features</a> the <a href="https://www.ctulocal1.org/posts/ctu-statement-on-chicago-city-council-passage-of-mayors-austerity-budget/">reopening of the six clinics</a> as a <a href="https://twitter.com/RossanaFor33/status/1199572139235520512">central theme</a>. Which raises the question: what exactly would reopening these facilities entail?</p>
<h2 id="putting-on-a-clinic">Putting on a clinic</h2>
<p>A primary argument from the “reopen” crowd is centered around the proximity of these clinics to the patients they served. A <a href="https://www.chicagotribune.com/politics/ct-met-lori-lightfoot-chicago-mental-health-clinics-20190524-story.html">Tribune piece</a> recounts the experience of one former patient, Jeanette Hanson, whose struggles to control her schizophrenia culminated in her 2014 death (having lost a family member to schizophrenia, this hits particularly close to home for me.)</p>
<p>Her case is both tragic and instructive. After the Beverly-Morgan Park clinic closed, Hanson was told to go to Roseland clinic, approximately three miles (or a 20 minute bus ride) away. But that “meant she had to take a bus ride through an area Hanson was afraid of, rationally or not, and she soon stopped going.” While she was certainly blameless for her inability commute, her experience does have strong implications for the distribution of clinics across the city needed to adequately serve patients with similar disorders.</p>
<p>Assuming Chicago is an approximately 10 mile by 20 mile rectangle, in order to make sure there is a clinic within approximately 1.5 mile radius of every patient like Hanson, Lightfoot would need to open at least 12 new clinics, in addition reopening the original six (though the math of covering rectangles with circles is <a href="https://stackoverflow.com/questions/7716460/fully-cover-a-rectangle-with-minimum-amount-of-fixed-radius-circles">surprisingly difficult</a>!). (Note how similar this number is to her actual “20 public and nonprofit health centers to expand care in high-need neighborhoods” plan mentioned above.)</p>
<p>A second, darker observation about Hanson’s story is that, whatever the damage caused by closing the clinics, it’s mostly done by now. After eight years, arguments that still prioritize the routines of former patients are (in some cases tragically) outdated. They have likely found care elsewhere (or not), their preferred doctors have moved on (or retired), and even the majority of the buildings are now occupied by new tenants (click on the red markers in the map above).</p>
<p>From my perspective, the main question is this: if we were starting from scratch, would we open six identical clinics in the same locations as the ones closed in 2011? I’m not an expert in public health, city planning, political calculus, etc., but it seems plausible to me that, going forward, the best plan is one <a href="https://chicago.suntimes.com/2019/4/29/18619801/why-lori-lightfoot-should-not-reopen-six-mental-health-clinics">tailored to fit our current set of opportunities and challenges</a>, not those from nearly a decade ago.</p>
<h1 id="but-what-about-the-police">But what about the police?</h1>
<p>A second <a href="https://twitter.com/soit_goes/status/1199370452512235525">popular strain of criticism</a> compares the increase in <a href="https://www.facebook.com/photo.php?fbid=2842946799072678&set=a.336305743070142&type=3&theater">spending on police</a> to the increase in mental health funding. Given their <a href="/lightfoot-prospiracy">vociferous opposition</a> to Lightfoot’s candidacy, it’s perhaps not surprising that activists like <a href="https://twitter.com/soit_goes">@soit_goes</a> and <a href="https://www.facebook.com/hayeskelly">Kelly Hayes</a> continue to deride her as mayor. But is there any merit to this particular criticism?</p>
<p><a href="https://twitter.com/soit_goes/status/1199370452512235525"><img src="/assets/images/chicago-budget/soit_goes.png" alt="soit_goes.png" title="soit_goes" class="center-image" /></a></p>
<p>Whether or not Chicago needs such a large police force (even by the standards of <a href="https://www.statista.com/chart/10593/how-much-do-us-cities-spend-on-policing/">comparable American cities</a>) is a matter of some personal uncertainty, and probably outside the scope of this post. What is surprising, however, is how radical Lightfoot’s $9.3 million dollar increase in mental health spending is, relative to the small increase in the police budget. To get a sense for this, I took a <a href="https://gist.github.com/danwahl/2b4746e8ac4e566cac79e2e53520d0a1">deep dive into Chicago budgets</a> dating back to 2013:</p>
<p><a href="/assets/images/chicago-budget/appropriations-by-function.png"><img src="/assets/images/chicago-budget/appropriations-by-function.png" alt="appropriations-by-function.png" title="appropriations-by-function" class="center-image" /></a></p>
<p>The above graph visualizes all “local” (non-grant) spending in Chicago. Removing functions like General Financing Requirements and Deductions gives a slightly better idea of where we put our money:</p>
<p><a href="/assets/images/chicago-budget/appropriations-by-function2.png"><img src="/assets/images/chicago-budget/appropriations-by-function2.png" alt="appropriations-by-function2.png" title="appropriations-by-function2" class="center-image" /></a></p>
<p>Spending is dominated by Public Safety (which incudes the Chicago Police Department), but besides a general trend of increasing appropriations, it’s still not clear if we can draw any conclusions about Lightfoot’s 2020 budget. Let’s zoom in on Public Safety and Community Services (which includes Mental Health Services):</p>
<p><a href="/assets/images/chicago-budget/public-safety-vs-community-services.png"><img src="/assets/images/chicago-budget/public-safety-vs-community-services.png" alt="public-safety-vs-community-services.png" title="public-safety-vs-community-services" class="center-image" /></a></p>
<p>Public Safety and Community Services spending both increase in the 2020 budget, and because their scales are so different, you’d be forgiven for thinking there is nothing interesting to see here. But let’s instead compare spending in each function relative to 2013 levels:</p>
<p><a href="/assets/images/chicago-budget/public-safety-vs-community-services-relative.png"><img src="/assets/images/chicago-budget/public-safety-vs-community-services-relative.png" alt="public-safety-vs-community-services-relative.png" title="public-safety-vs-community-services-relative" class="center-image" /></a></p>
<p>Here we can see that both Community Services and overall spending (Grand Total) have increased faster than Public Safety, with an especially sharp jump in 2020. Let’s use the same method, but directly compare the Chicago Police Department with the Department of Public Health, which oversees Mental Health Services:</p>
<p><a href="/assets/images/chicago-budget/police-vs-public-health-relative.png"><img src="/assets/images/chicago-budget/police-vs-public-health-relative.png" alt="police-vs-public-health-relative.png" title="police-vs-public-health-relative" class="center-image" /></a></p>
<p>Based on this graph, the criticism of Emanuel appears justified, with public health spending lagging behind both general and police allocations for the entirety of his two terms. However, it’s clear that Lightfoot’s 2020 budget represents a drastic improvement, with a <em>more than 50% jump</em> in funding for the Department of Public Health. Narrowing in on Mental Health Services makes for an even starker contrast:</p>
<p><a href="/assets/images/chicago-budget/police-vs-mental-health-relative.png"><img src="/assets/images/chicago-budget/police-vs-mental-health-relative.png" alt="police-vs-mental-health-relative.png" title="police-vs-mental-health-relative" class="center-image" /></a></p>
<p>In fact, the <strong>70% year-over-year increase in Mental Health Services</strong> is larger than any single-function spending increase in Chicago’s budget since my dataset began in 2013, and is much larger than the (apparently nominal) 6% increase in police spending. If this increase holds over the next two years of her term, she will have fulfilled the spirit of her pledge to increase mental health spending by more than $25 million, if not the actual promise to reopen six specific clinics.</p>
<p>Could Lightfoot have spent even more on mental health, and defunded a potentially bloated police force? Perhaps, but she could also have allocated the additional $9.3 million to the police, and any coherent political ideology should at least recognize the magnitude of this difference for the progressive victory that it is.</p>
<h1 id="closing-thoughts">Closing thoughts</h1>
<p>I dislike it when people use terms to describe what they’re <em>not</em> instead of what they <em>are</em>, but after covering two rounds of political controversies surrounding Lori Lightfoot, I want a term to at least describe the kind of progressive I <em>aspire not to be</em>. The closest I can come up with is “conflict theory socialist”, which is probably in the Authoritarian Left quadrant of the <a href="https://www.politicalcompass.org/">traditional political compass</a>, or on top of John Nerst’s <a href="https://everythingstudies.com/2019/03/25/the-tilted-political-compass-part-2-up-and-down/">tilted political compass</a>.</p>
<p>Lightfoot’s budget is the kind of incremental improvement that’s stereotypically eschewed by socialists, but it feels like more than just socialism that is driving her harshest critics, and conflict theory seems to explain the remainder of the variance. I think Lightfoot’s biggest mistake in this process was probably her initial, premature promise to reopen the clinics, which gave her political opponents fodder come budget time, but (if I’m right about conflict theory) had little chance of winning them over.</p>
<p>I’m not sure what force binds conflict theory and socialism so tightly (on Twitter, at least), but the term “conflict theory socialist” itself seems to imply a counterpart, the elusive “mistake theory socialist”. Since Nathan Robinson went <a href="https://www.currentaffairs.org/2019/10/why-bernie-has-to-win/">off</a> <a href="https://www.currentaffairs.org/2019/11/pelosi-must-go/">the</a> <a href="https://www.currentaffairs.org/2019/11/i-dont-know-why-i-should-care-what-the-constitution-says/">rails</a> this election cycle, however, I’m having trouble identifying any in the wild (on Twitter, at least). What would a modern mistake theory socialist even look like?</p>
<p>Maybe it’s Elizabeth Warren, who is currently <a href="https://thehill.com/hilltv/rising/472234-saagar-enjeti-warren-loses-half-her-support-after-bungled-m4a-rollout">suffering the political consequences</a> of <a href="https://elizabethwarren.com/plans/paying-for-m4a">applying mathematical rigor</a> to Sanders’s Medicare For All plan. Or maybe it’s the <a href="https://www.effectivealtruism.org/">Effective Altruism</a> movement, which applies <a href="https://eachicago.com/key-terms/">consequentialist (rather than deontological) philosophy</a> to enact “effective” social change. Or, ironically, maybe it’s even the <a href="https://neoliberalproject.org/">Neoliberal Project</a>, which advocates for many of the same social goals, but through policies like <a href="https://www.reddit.com/r/neoliberal/wiki/index#wiki_neoliberal_dogma">free trade, open borders, and carbon pricing</a>.</p>
<p>So rather than say what I’m not, let me instead be the first to publicly identify as a “mistake theory socialist” (a term that <a href="https://www.google.com/search?q="mistake+theory+socialist"">currently returns</a> zero search results!). And, uh, if you have any idea what that entails, please <a href="hi@danwahl.net">let me know</a>.</p>Dan WahlInvestigating the progressive case against Chicago's 2020 budget.My Top Ten Slate Star Codex Posts2019-10-07T00:00:00+00:002019-10-07T00:00:00+00:00https://danwahl.net/blog/slate-star-codex<p>For my group of middle school friends, a popular lunchtime activity was to create “funny” top ten lists in the <a href="https://www.youtube.com/watch?v=Wa_AXXkQaHQ">tradition of David Letterman</a>. Mercifully, I remember almost nothing about the content of these lists, except for our (my?) inexplicable habit of trying to include then-Attorney-General Janet Reno in each of them (clearly <a href="https://www.reddit.com/r/iamveryrandom/">r/iamveryrandom</a>).</p>
<p>Last weekend I attended the <a href="https://slatestarcodex.com/2019/09/27/chicago-meetup-this-saturday">Slate Star Codex (SSC) meetup in Chicago</a>, and got to (briefly) meet one of my personal heroes, <a href="https://slatestarcodex.com/about/">Scott Alexander</a>. My excitement about this has been difficult to convey to others, since few of my friends, family, and coworkers even know who he is. And that’s a shame, because his forward-thinking, creative, humorous, and <em>comprehensive</em> style of writing is a great antidote for… well, the rest of the internet.</p>
<p>One difficulty in becoming a new SSC reader is that Scott is <em>prolific</em>, somehow averaging a post every other day (compare that to a post every other month on this blog), and it can be hard to know where to begin. And so, in the spirit of a middle school lunch period (sans Janet Reno, RIP), I present my top ten favorite SSC posts.</p>
<!--more-->
<ul id="markdown-toc">
<li><a href="#10-book-review-the-secret-of-our-success" id="markdown-toc-10-book-review-the-secret-of-our-success">10. Book Review: The Secret Of Our Success</a></li>
<li><a href="#9-the-hour-i-first-believed" id="markdown-toc-9-the-hour-i-first-believed">9. The Hour I First Believed</a></li>
<li><a href="#8-meditations-on-moloch" id="markdown-toc-8-meditations-on-moloch">8. Meditations On Moloch</a></li>
<li><a href="#7-a-thrivesurvive-theory-of-the-political-spectrum" id="markdown-toc-7-a-thrivesurvive-theory-of-the-political-spectrum">7. A Thrive/Survive Theory Of The Political Spectrum</a></li>
<li><a href="#6-introducing-unsong" id="markdown-toc-6-introducing-unsong">6. Introducing Unsong</a></li>
<li><a href="#5-nobody-is-perfect-everything-is-commensurable" id="markdown-toc-5-nobody-is-perfect-everything-is-commensurable">5. Nobody Is Perfect, Everything Is Commensurable</a></li>
<li><a href="#4-conflict-vs-mistake" id="markdown-toc-4-conflict-vs-mistake">4. Conflict Vs. Mistake</a></li>
<li><a href="#3-the-whole-city-is-center" id="markdown-toc-3-the-whole-city-is-center">3. The Whole City Is Center</a></li>
<li><a href="#2-social-justice-and-words-words-words" id="markdown-toc-2-social-justice-and-words-words-words">2. Social Justice And Words, Words, Words</a></li>
<li><a href="#1-fear-and-loathing-at-effective-altruism-global-2017" id="markdown-toc-1-fear-and-loathing-at-effective-altruism-global-2017">1. Fear And Loathing At Effective Altruism Global 2017</a></li>
<li><a href="#honorable-mentions" id="markdown-toc-honorable-mentions">Honorable Mentions</a></li>
</ul>
<h1 id="10-book-review-the-secret-of-our-success">10. <a href="https://slatestarcodex.com/2019/06/04/book-review-the-secret-of-our-success/">Book Review: The Secret Of Our Success</a></h1>
<p>This review is part of a sprawling series of posts about cultural evolution, covering everything from <a href="https://slatestarcodex.com/2019/06/03/repost-epistemic-learned-helplessness/">Epistemic Learned Helplessness</a> to <a href="https://slatestarcodex.com/2019/06/06/asymmetric-weapons-gone-bad/">Asymmetric Weapons Gone Bad</a>. Specifically, Scott’s review of <a href="https://www.goodreads.com/book/show/25761655-the-secret-of-our-success">Joseph Henrich’s 2015 book</a> gave me a new appreciation for evolved cultural knowledge, encoded as tradition and protected by <a href="https://en.wikipedia.org/wiki/Wikipedia:Chesterton%27s_fence">Chesterton’s fence</a>. For example, on the preparation of manioc root:</p>
<blockquote>
<p>A reasonable person would have asked why everyone was wasting so much time preparing manioc. When told “Because that’s how we’ve always done it”, they would have been unsatisfied with that answer. They would have done some experiments, and found that a simpler process of boiling it worked just as well. They would have saved lots of time, maybe converted all their friends to the new and easier method. Twenty years later, they would have gotten sick and died, in a way so causally distant from their decision to change manioc processing methods that nobody would ever have been able to link the two together.</p>
</blockquote>
<h1 id="9-the-hour-i-first-believed">9. <a href="https://slatestarcodex.com/2018/04/01/the-hour-i-first-believed/">The Hour I First Believed</a></h1>
<p>As atheist who was raised Catholic, I’ve always had <a href="/kidney-donation">mixed feelings</a> about religion, but they got considerably more complicated after reading this post. Scott starts by introducing five concepts (Acausal trade, Value handshakes, Counterfactual mugging, Simulation capture, and The Tegmarkian multiverse), and combines them to form the most compelling argument <em>for</em> the existence of a moral God that I’ve ever encountered (though not as concise as Douglas Adams’s <a href="https://www.goodreads.com/quotes/35681-now-it-is-such-a-bizarrely-improbable-coincidence-that-anything">proof of the non-exitence of God</a>). From the section on Simulation capture:</p>
<blockquote>
<p>…you have created a superintelligent AI and trapped it in a box. All it can do is compute and talk to you. How does it convince you to let it out?</p>
<p>It might say “I’m currently simulating a million copies of you in such high fidelity that they’re conscious. If you don’t let me out of the box, I’ll torture the copies.”</p>
<p>You say “I don’t really care about copies of myself, whatever.”</p>
<p>It says “No, I mean, I did this five minutes ago. There are a million simulated yous, and one real you. They’re all hearing this message. What’s the probability that you’re the real you?”</p>
<p>Since (if it’s telling the truth) you are most likely a simulated copy of yourself, all million-and-one versions of you will probably want to do what the AI says, including the real one.</p>
</blockquote>
<h1 id="8-meditations-on-moloch">8. <a href="https://slatestarcodex.com/2014/07/30/meditations-on-moloch/">Meditations On Moloch</a></h1>
<p>In <a href="https://thehill.com/blogs/blog-briefing-room/news/464073-jeffress-suggests-democrats-worship-pagan-god-moloch-who">recent political news</a>, Pastor (and member of Trump’s Evangelical Advisory Board) Robert Jeffress accused Democrats of worshipping “the pagan god of the Old Testament Moloch, who allowed for child sacrifice” in response to the opening of an impeachment inquiry. This oddly specific accusation has, in the way of the internet, been taken up as a badge of honor among <a href="https://activistmommy.com/democrats-mock-todd-starnes-by-shamelessly-praising-moloch-on-twitter/">actual godless liberal elites</a>. And I’m not sure whether this has anything to do with Meditations On Moloch, but if I listen hard enough I can <em>almost</em> hear Ginsberg’s saccharine voice (or maybe <a href="https://www.youtube.com/watch?v=5tRXcjSTFWM&t=1823s">Glass’s weird chorus</a>) on the wind:</p>
<blockquote>
<p>Moloch the incomprehensible prison! Moloch the crossbone soulless jailhouse and Congress of sorrows! Moloch whose buildings are judgment! Moloch the vast stone of war! Moloch the stunned governments!</p>
</blockquote>
<p>(Note: consider listening to the <a href="http://sscpodcast.libsyn.com/meditations-on-moloch">podcast version</a> of this one, which splices in a live recording of Ginsberg reading <a href="https://poets.org/poem/howl-parts-i-ii">Howl II</a>.)</p>
<h1 id="7-a-thrivesurvive-theory-of-the-political-spectrum">7. <a href="https://slatestarcodex.com/2013/03/04/a-thrivesurvive-theory-of-the-political-spectrum/">A Thrive/Survive Theory Of The Political Spectrum</a></h1>
<p>Political compass memes are popular in <a href="https://www.reddit.com/r/PoliticalCompassMemes/">my corner of the internet</a>, and while the <a href="https://www.politicalcompass.org/uselection2020">actual website</a> might be a joke (I bet you didn’t realize that Elizabeth Warren is a right-wing authoritarian), it does raise an interesting question: if liberals are left and conservatives are right, what does the up and down axis represent? Thrive/Survive begins to answer that question (more below in Conflict Vs. Mistake), and also provides a visceral example of conservative thinking that I actually empathized with:</p>
<blockquote>
<p>I propose that the best way for leftists to get themselves in a rightist frame of mind is to imagine there is a zombie apocalypse tomorrow. It is a very big zombie apocalypse and it doesn’t look like it’s going to be one of those ones where a plucky band just has to keep themselves alive until the cavalry ride in and restore order. This is going to be one of your long-term zombie apocalypses. What are you going to want?</p>
<p>First and most important, guns. Lots and lots of guns.</p>
</blockquote>
<h1 id="6-introducing-unsong">6. <a href="https://slatestarcodex.com/2015/12/30/introducing-unsong/">Introducing Unsong</a></h1>
<p>This one is cheating, because it’s just the first chapter of Scott’s magical serialized novel <a href="http://unsongbook.com/">Unsong</a>. While it is, at times, clearly the unedited first effort of an hobbyist fiction writer, Unsong is also my favorite science fiction story in years. Starting with the premise that the bible is literally true, Scott expands a proto-love story into an adventure that explores everything from asexuality to telepathy to AI to actual hell, and winds up providing the most convincing universal origin story since Asimov’s <a href="https://www.multivax.com/last_question.html">The Last Question</a>. Even as an atheist, by the end I wanted it to be true.</p>
<blockquote>
<p>Two minutes left till lunar sunrise broke the connection. The astronauts’ only orders from NASA had been to “do something appropriate”</p>
<p>“In the beginning,” read Bill Anders, “God created the heaven and the earth. And the earth was without form, and void; and darkness was upon the face of the deep.”</p>
<p>So for two minutes on Christmas Eve, while a billion people listened, three astronauts read the Book of Genesis from a tiny metal can a hundred miles above the surface of the moon.</p>
<p>Then, mid-sentence, they crashed into the crystal sphere surrounding the world, because it turned out there were far fewer things in Heaven and Earth than were dreamt of in almost anyone’s philosophy.</p>
</blockquote>
<h1 id="5-nobody-is-perfect-everything-is-commensurable">5. <a href="https://slatestarcodex.com/2014/12/19/nobody-is-perfect-everything-is-commensurable/">Nobody Is Perfect, Everything Is Commensurable</a></h1>
<p>At the SSC Meetup in Chicago, I had a conversation with a fellow reader about EA cause prioritization, and specifically <a href="https://forum.effectivealtruism.org/posts/BY8gXSpGijypbGitT/why-i-prioritize-moral-circle-expansion-over-artificial">Moral Circle Expansion</a>. My enthusiasm for MCE as a kind of meta-cause (positively influencing other present and future causes) was met with some skepticism, and I had a hard time conjuring non-axiomatic arguments in its defense.</p>
<p>One of the problems with advocating for altruism, generally, is that people understand the <a href="https://en.wikipedia.org/wiki/First-mover_advantage#First-mover_disadvantages">first-mover disadvantage</a> on a gut level, and it’s hard to persuade them to act individually without a guarantee that others will do the same. This has the side effect of deflating moral circles, and causing others to help only those with whom they expect reciprocity.</p>
<p>In this post, Scott makes the argument for taking the <a href="https://www.givingwhatwecan.org/pledge/">Giving What We Can pledge</a> as a way of circumventing the problem by conforming to the achievable, effective giving threshold of 10%:</p>
<blockquote>
<p>Nobody is perfect. This gives us license not to be perfect either. Instead of aiming for an impossible goal, falling short, and not doing anything at all, we set an arbitrary but achievable goal designed to encourage the most people to do as much as possible. That goal is ten percent.</p>
<p>Everything is commensurable. This gives us license to determine exactly how we fulfill that ten percent goal. Some people are triggered and terrified by politics. Other people are too sick to volunteer. Still others are poor and cannot give very much money. But money is a constant reminder that everything goes into the same pot, and that you can fulfill obligations in multiple equivalent ways.</p>
</blockquote>
<h1 id="4-conflict-vs-mistake">4. <a href="https://slatestarcodex.com/2018/01/24/conflict-vs-mistake/">Conflict Vs. Mistake</a></h1>
<p>In the Conflict Vs. Mistake dichotomy (a companion piece to Thrive/Survive above), Scott provides a critical insight into the last three years of American politics; the correct vertical axis of the political compass:</p>
<blockquote>
<p>Mistake theorists think a Revolution is stupid. After the proletariat (or the True Patriotic Americans, or whoever) have seized power, they’re still faced with the same set of policy problems we have today, and no additional options. Communism is intellectually bankrupt since it has no good policy prescriptions for a communist state. If it did have good policy prescriptions for a communist state, we could test and implement those policies now, without a revolution. Karl Marx could have saved everyone a lot of trouble by being Bernie Sanders instead.</p>
<p>Conflict theorists think a technocracy is stupid. Whatever the right policy package is, the powerful will never let anyone implement it. Either they’ll bribe the technocrats to parrot their own preferences, or they’ll prevent their recommendations from carrying any force. The only way around this is to organize the powerless to defeat the powerful by force – after which a technocracy will be unnecessary. Bernie Sanders could have saved himself a lot of trouble by realizing everything was rigged against him from the start and becoming Karl Marx</p>
</blockquote>
<p>(Other candidates for the vertical axis include <a href="https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow">System 1 and 2</a>, <a href="https://everythingstudies.com/2018/05/25/decoupling-revisited/">de/couplers</a>, and <a href="https://en.wikipedia.org/wiki/Prisoner%27s_dilemma">game theory cooperators/defectors</a>. I think these are all basically the same thing, and that Thrive/Survive is the “Conflict/System 1/coupler/defector” response on the left/right.)</p>
<h1 id="3-the-whole-city-is-center">3. <a href="https://slatestarcodex.com/2018/07/18/the-whole-city-is-center/">The Whole City Is Center</a></h1>
<blockquote>
<p>It’s not a coincidence that we hold people responsible for traits and actions that are precisely the traits and actions that, through praise and blame, we can modify.</p>
</blockquote>
<p><cite>— <a href="https://samharris.org/podcasts/abusing-dolores/">Paul Bloom</a></cite></p>
<blockquote>
<p>Alcoholism is a disease, but it’s the only disease you can get yelled at for having. “God damnit, Otto, you’re an alcoholic!” “God damnit, Otto, you have lupus!” One of those two doesn’t sound right.</p>
</blockquote>
<p><cite>— <a href="http://www.cc.com/video-clips/annb96/comedy-central-presents-alcoholism">Mitch Hedberg</a></cite></p>
<p>If you like Socratic dialogues and have ever had the thought “there is no such thing as <em>blank</em>, it’s just an outdated social construct that causes harm” then this post is for you:</p>
<blockquote>
<p><strong>Simplicio:</strong> If there were such a thing as laziness, but it was rare, then it would make sense to argue “most people aren’t lazy”, since lazy would be pointing at a particular quality that most people don’t have. But if you say there’s no such thing as laziness, then it sounds like maybe you’re kind of weird to insist on defining “laziness” to refer a quality that nobody has, yet refuse to use any word to refer to the quality that many people do have. It would be like wanting our language to have a word for “unicorn” but not for “horse”.</p>
</blockquote>
<h1 id="2-social-justice-and-words-words-words">2. <a href="https://slatestarcodex.com/2014/07/07/social-justice-and-words-words-words/">Social Justice And Words, Words, Words</a></h1>
<p>I’m actively involved in several social-justice-adjacent organizations and friend groups, but (ironically) I didn’t have the words to describe my frustrations with certain aspects the movement until reading this post. Scott introduces the (immensely useful) <a href="philpapers.org/archive/SHATVO-2.pdf">Motte and Bailey Doctrine</a> as an analogy for the type of bait-and-switch language games that some social justice proponents deploy (alongside call-out culture) as a brute-force tactic for acquiring power.</p>
<p>For sports fans, this is akin to offsides in soccer, where the defensive back line will lure the offense downfield, only to simultaneously charge forward, leaving the opposition stranded at the critical moment:</p>
<blockquote>
<p>By this metaphor, statements like “God is an extremely powerful supernatural being who punishes my enemies” or “The Sky Ox theory and the nuclear furnace theory are equally legitimate” or “Men should not be allowed to participate in discussions about gender” are the bailey – not defensible at all, but if you can manage to hold them you’ve got it made.</p>
<p>Statements like “God is just the order and love in the universe” and “No one perceives reality perfectly directly” and “Men should not interject into safe spaces for women” are the motte – extremely defensible, but useless.</p>
<p>As long as nobody’s challenging you, you spend time in the bailey reaping the rewards of occupying such useful territory. As soon as someone challenges you, you retreat to the impregnable motte and glare at them until they get annoyed and go away. Then you go back to the bailey.</p>
</blockquote>
<h1 id="1-fear-and-loathing-at-effective-altruism-global-2017">1. <a href="https://slatestarcodex.com/2017/08/16/fear-and-loathing-at-effective-altruism-global-2017/">Fear And Loathing At Effective Altruism Global 2017</a></h1>
<p>Nominally a review of a two-year-old conference (thrilling stuff), this post is actually a defense of everything that I hold dear. I find myself reading it every few months as a reminder that there is good in the world, and that smart people are working on making things better. I think it’s the most moving of Scott’s posts to date, especially his treatment of the EA movement’s underlying utilitarian philosophy, which is often stereotyped as cold and calculating:</p>
<blockquote>
<p>But every so often, I can see the world as they have to. Where the very existence of suffering, any suffering at all, is an immense cosmic wrongness, an intolerable gash in the world, distressing and enraging. Where a single human lifetime seems frighteningly inadequate compared to the magnitude of the problem. Where all the normal interpersonal squabbles look trivial in the face of a colossal war against suffering itself, one that requires a soldier’s discipline and a general’s eye for strategy.</p>
<p>All of these Effecting Effective Effectiveness people don’t obsess over efficiency out of bloodlessness. They obsess because the struggle is so desperate, and the resources so few. Their efficiency is military efficiency. Their cooperation is military discipline. Their unity is the unity of people facing a common enemy. And they are winning. Very slowly, WWI trench-warfare-style. But they really are.</p>
</blockquote>
<h1 id="honorable-mentions">Honorable Mentions</h1>
<ul>
<li><a href="https://slatestarcodex.com/2013/04/06/polyamory-is-boring/">Polyamory Is Boring</a>: It is, but in a good way!</li>
<li><a href="https://slatestarcodex.com/2014/12/17/the-toxoplasma-of-rage/">The Toxoplasma Of Rage</a>: When it comes to meme virality, the outrage is the point.</li>
<li><a href="https://slatestarcodex.com/2018/05/16/basic-income-not-basic-jobs-against-hijacking-utopia/">Basic Income, Not Basic Jobs: Against Hijacking Utopia</a>: Contra Booker on UBJ and mindless drudgery.</li>
<li><a href="https://slatestarcodex.com/2018/08/23/carbon-dioxide-an-open-door-policy/">Carbon Dioxide: An Open Door Policy</a>: I sleep with my windows open as a result of reading this post.</li>
<li><a href="https://slatestarcodex.com/2018/09/12/in-the-balance/">In The Balance</a>: On how to find the meta-balance between balance and excess.</li>
<li><a href="https://slatestarcodex.com/2018/12/19/refactoring-culture-as-branch-of-government/">Refactoring: Culture As Branch Of Government</a>: On why democratization projects are often doomed from the outset.</li>
<li><a href="https://slatestarcodex.com/2019/03/04/prospiracy-theories/">Prospiracy Theories</a>: A clever spin on <a href="/lightfoot-prospiracy#prospiracy-theory">conspiratorial thinking</a>.</li>
<li><a href="https://slatestarcodex.com/2019/07/29/against-against-billionaire-philanthropy/">Against Against Billionaire Philanthropy</a>: How many lives is your stance against billionaires worth?</li>
</ul>Dan Wahlor Who is Scott Alexander?Hold the Reins2019-08-03T00:00:00+00:002019-08-03T00:00:00+00:00https://danwahl.net/blog/kidney-donation-2<p><strong>Edit 2019-08-12: After listening to Jeremiah Johnson and Rob Wiblin discuss kidney donation on the <a href="https://neoliberalproject.org/podcast">Neoliberal Podcast</a>, I decided to revise this post with <a href="/kidney-donation-2#update">a few moderate updates</a>.</strong></p>
<p>I follow <a href="https://www.instagram.com/p/B0REK7GBfMH/">thechicrew</a> (a vegan <a href="https://microsanctuary.org/">microsanctuary</a>) on Instagram mostly for the cute animal pics, but lately an unrelated storyline has been developing on their feed. One of the caretakers, Jay, recently donated a kidney to an unknown 14-year-old recipient. From the <a href="https://www.instagram.com/p/B0A6Pl0pjP9/?utm_source=ig_web_copy_link">announcement post</a>:</p>
<blockquote>
<p>Kidney disease is the 9th leading cause of death in the U.S., more common than breast or prostate cancer deaths. It seems painfully obvious to me that those who can donate SHOULD. I know Jay has inspired me to see if I’m eligible and if you’re also inspired, you can learn more at kidney.org. ❤</p>
</blockquote>
<p>Jay’s is not the first altruistic kidney donation I’ve encountered. Peter Singer made the case for kidney donation in his <a href="https://www.ted.com/talks/peter_singer_the_why_and_how_of_effective_altruism?language=en">2013 TED Talk</a>, Dylan Matthews documented his own donation process on <a href="https://www.vox.com/science-and-health/2017/4/11/12716978/kidney-donation-dylan-matthews">Vox’s Future Perfect podcast</a>, and I even <a href="/kidney-donation">met an altruistic donor</a> myself at EA Global 2016.</p>
<p>These stories have been an inspiration to me, and as a result I’ve been strongly considering living donation. But <a href="https://www.effectivealtruism.org/doing-good-better/">“Doing Good Better”</a> asks us to look beyond mere inspiration and consider the aggregate impacts of our actions. Is it really “painfully obvious” that “those who can donate SHOULD”? Let’s do the math.</p>
<!--more-->
<ul id="markdown-toc">
<li><a href="#recipient-outcomes" id="markdown-toc-recipient-outcomes">Recipient outcomes</a></li>
<li><a href="#donor-outcomes" id="markdown-toc-donor-outcomes">Donor outcomes</a></li>
<li><a href="#non-kidney-donations" id="markdown-toc-non-kidney-donations">Non-kidney donations</a></li>
<li><a href="#conclusion" id="markdown-toc-conclusion">Conclusion</a></li>
<li><a href="#update" id="markdown-toc-update">Update</a></li>
</ul>
<h1 id="recipient-outcomes">Recipient outcomes</h1>
<p>To quantify the benefits of kidney donation, I used numbers from <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/ajt.14702">“An economic assessment of contemporary kidney transplant practice”</a>, which uses a Markov model simulation of recipient outcomes in terms of <a href="https://en.wikipedia.org/wiki/Quality-adjusted_life_year">quality-adjusted life years</a> (QALYs) to generate the following table:</p>
<p><a href="/assets/images/kidney-donation-2/recipient.png"><img src="/assets/images/kidney-donation-2/recipient.png" alt="recipient.png" title="recipient" class="center-image" /></a></p>
<p>In the ideal case, the two columns of interest are “HLA 0-3 mismatch LDKT” (a living donor kidney transplant with a the minimal number of <a href="https://en.wikipedia.org/wiki/Human_leukocyte_antigen">human leukocyte antigens</a> mismatches) and “Dialysis” (a non-transplant therapy to replace kidney function). Using the “Average QALY over 10 years” row, transplanting a kidney from a living donor to a dialysis patient will realize a median benefit of <code class="language-plaintext highlighter-rouge">7.07 - 4.45 = 2.62 QALYs</code>.</p>
<p>The 10 year time horizon is not unreasonable, but given that donated kidneys <a href="http://www.kidneyfund.org/kidney-disease/kidney-failure/treatment-of-kidney-failure/kidney-transplant/deceased-donor-transplant.html#how-long_will_my_new_kidney_last">last an average of 15 years</a>, let’s multiply by a factor of <code class="language-plaintext highlighter-rouge">1.5</code> to get a total benefit of around <strong>4 QALYs</strong>.</p>
<h1 id="donor-outcomes">Donor outcomes</h1>
<p>This benefit comes at a cost, however, since the donation procedure is not without risk. As with any major operation, there is a small chance of perioperative death, affecting approximately <a href="https://www.kidney.org/transplantation/livingdonors/risks-of-surgery">3 in every 10,000 donors</a>.</p>
<p>But there are other concerns as well. Even healthy kidney donors become slightly more prone to end-stage renal disease (ESRD), as described in <a href="https://bmjopen.bmj.com/content/7/8/e016490">“Lifetime risks of kidney donation: a medical decision analysis”</a>. The paper, which also used a Markov model, simulated outcomes for 40-year-old donors of varying race and gender:</p>
<p><a href="/assets/images/kidney-donation-2/donor.png"><img src="/assets/images/kidney-donation-2/donor.png" alt="donor.png" title="donor" class="center-image" /></a></p>
<p>For the typical white, male 40-year-old, kidney donation results in a loss of approximately 0.272 QALYs, or 1.24% of the remaining quality years. Adding in the small probability of immediate death (<code class="language-plaintext highlighter-rouge">3/10,000*21.871</code>), and adjusting for my current age (35), I’ll round this number up to approximately <strong>0.3 QALYs</strong> lost.</p>
<h1 id="non-kidney-donations">Non-kidney donations</h1>
<p>On the surface then, living kidney donation does seem like the “painfully obvious” choice, since the procedure generates an average of 4 QALYs of benefit for recipient and costs only 0.3 QALYs to the donor, for a net benefit of <strong>3.7 QALYs</strong>. The picture gets murkier, however, when non-kidney donations are included. Consider the following:</p>
<ul>
<li><a href="https://www.givingwhatwecan.org/about-us/">Giving What We Can</a> (GWWC) is an Effective Altruism (EA) organization which encourages members to donate 10% of their earnings to effective charities. Over the 10 years since its inception, they have tracked $126,751,939 in donations from their 4,210 members, for an average yearly donation of <code class="language-plaintext highlighter-rouge">$126,751,939/4,210/10 =~ $3,000</code> per member. Note that this estimate is likely conservative, since some members (like me!) don’t use the GWWC site to track their donations.</li>
<li><a href="https://www.givewell.org/">GiveWell</a>, another EA organization, reviews giving opportunities and provides a yearly list of <a href="https://www.givewell.org/charities/top-charities">the most cost-effective organizations</a>. In 2018 <a href="https://www.givewell.org/how-we-work/our-criteria/cost-effectiveness/cost-effectiveness-models">they estimated</a> that The Against Malaria Foundation (AMF) prevents the death of an individual under the age of 5 (through the distribution of insecticide-treated bed nets) for every $3,070 donated, using “conventional” assumptions.</li>
<li>In <a href="https://docs.google.com/document/d/1hx7q7cIQdXd9dKB9WvlSSCdGKYk8jRB9xjyp8kIWzyE/edit#">a separate document</a>, GiveWell Senior Research Analyst <a href="https://blog.givewell.org/author/james-snowden/">James Snowden</a> calculated that averting the death of an individual under the age of 5 is worth 42.7 wellness-adjusted life years (WALYs, a more comprehensive version of QALYs that includes non-health states). However, in order to make this more directly comparable to the QALY numbers above, I’ll use only the “WALYs lost because of direct badness of death” portion, or 31.2 “QALYs”.</li>
<li>Finally, in addition to donor QALY losses, the “Lifetime risks” paper also calculates that the average white male 40-year-old will lose 0.767 years of life through the donation process. I’ll round this up to 1 year given the considerations listed in the previous section.</li>
</ul>
<p>Putting it all together, one year of a GWWC member’s life represents a $3,000 gift to an effective organization like AMF, which saves the life of a 5 year old for every $3,070 donation, for a average benefit of about <code class="language-plaintext highlighter-rouge">1*3,000/3,070*31.2 = 30.5 QALYs</code>! Or stating it another way, living kidney donation actually <em>costs</em> effective altruists up to (<code class="language-plaintext highlighter-rouge">3.7 - 30.5 =</code>) <strong>26.8 QALYs</strong> through lost monetary donations.</p>
<h1 id="conclusion">Conclusion</h1>
<p>Since not everyone is an effective altruist (yet), living kidney donation still seems like a good idea for most people. Jay has no regrets <a href="https://www.instagram.com/p/B0ejI9kJFFE/?utm_source=ig_web_copy_link">despite a difficult recovery process</a>, Dylan Matthews <a href="https://www.reddit.com/r/IAmA/comments/64z1p4/i_gave_my_kidney_to_a_stranger_ama/">seems to have had a positive experience</a>, and other donors from within the EA community <a href="https://www.nytimes.com/2011/12/06/opinion/why-selling-kidneys-should-be-legal.html">have ideas</a> for making the procedure even more beneficial.</p>
<p>There’s even the possibility that your non-directed donation could start a <a href="https://jamanetwork.com/journals/jamasurgery/fullarticle/1654855">kidney chain</a>, with cascading benefits that may reverse the above conclusions.</p>
<p>If it’s not for you, however, that’s fine too. But do consider joining GWWC regardless. As Slate Star Codex notes in <a href="https://slatestarcodex.com/2014/12/19/nobody-is-perfect-everything-is-commensurable/">Nobody Is Perfect, Everything Is Commensurable</a>:</p>
<blockquote>
<p>Nobody is perfect. This gives us license not to be perfect either. Instead of aiming for an impossible goal, falling short, and not doing anything at all, we set an arbitrary but achievable goal designed to encourage the most people to do as much as possible. That goal is ten percent.</p>
<p>Everything is commensurable. This gives us license to determine exactly how we fulfill that ten percent goal. Some people are triggered and terrified by politics. Other people are too sick to volunteer. Still others are poor and cannot give very much money. But money is a constant reminder that everything goes into the same pot, and that you can fulfill obligations in multiple equivalent ways. Some people will not be able to give ten percent of their income without excessive misery, but I bet thinking about their contribution in terms of a fungible good will help them decide how much volunteering or activism they need to reach the equivalent.</p>
</blockquote>
<h1 id="update">Update</h1>
<p>Living kidney donation has been a hot topic on my newsfeed recently, following <a href="https://medium.com/@_JeremiahJohnson/why-im-donating-a-kidney-and-why-you-should-consider-donating-as-well-8483d2ae0d29">Jeremiah Johnson’s donation</a> and some corresponding <a href="https://twitter.com/dylanmatt/status/1160746176255340545">Neoliberal Twitter buzz</a>:</p>
<p><a href="/assets/images/kidney-donation-2/tweet.png"><img src="/assets/images/kidney-donation-2/tweet.png" alt="tweet.png" title="tweet" class="center-image" /></a></p>
<p>After doing a little follow-up research, I wanted to expand on a few points that deserved more consideration in my original post.</p>
<p>First, I recommend reading Tom Ash’s entry in the Effective Altruism Forum, <a href="https://forum.effectivealtruism.org/posts/yTu9pa9Po4hAuhETJ/kidney-donation-is-a-reasonable-choice-for-effective">“Kidney donation is a reasonable choice for effective altruists and more should consider it”</a>. As you might expect from the title, Tom arrives at essentially the opposite conclusion, but with a more thorough (albeit potentially outdated?) analysis. The crux of our disagreement stems from the way we adjust for the reduced life expectancy of living donors. Tom provides a few calculations to show that the financial effects of mortality and ESRD are negligible, and argues that since any ensuing health risks are likely decades away, uncertainty (over “retirement, technological improvement, ability to receive a transplant, and defection from the EA cause”) swamps other considerations.</p>
<p>Although the reasoning here is interesting, I’m not sure it’s enough to shift the balance on its own. My calculations imply that typical EA giving for one additional year does an order of magnitude more good than kidney donation, in QALY terms. To even the outcomes is to assume that <strong>90%</strong> of the value of an additional year of life is somehow lost, potentially through one of the four factors Tom lists. I think(/hope) that the most likely of these is technological improvement, which in itself might be a good reason to consider donating immediately (while there are still QALYs to be had)!</p>
<p>Second, I’d be more persuaded that living donation is worth the risk if I was confident in the ex-ante expected length of the kidney chain I was starting. I contacted <a href="https://www.kidney.org/">NKF</a> and <a href="https://unos.org/">UNOS</a> about this, and eventually got the following response:</p>
<blockquote>
<p>Unfortunately there is no way to determine how long a chain will be prior to entering a KPD program. Even after a chain is found, there is no way in advance to determine if the entire chain will proceed to transplant, it will suddenly end with 1 or 2 transplants, or how long it may continue on for X number of additional transplants.</p>
</blockquote>
<p>All that being said, my opinion on living donation is shifting from “probably not worth it” to “maybe not a bad idea.” As the tweets I linked to above imply, there might be good non-QALY reasons for donations, such as capturing the moral high ground for future debate (only sort of kidding). In part to gather more information, I’ve started the <a href="https://nkr.donorscreen.org/register/donate-kidney">donor screening process</a>, and will likely post another update here soon.</p>Dan WahlWhy effective altruists should consider keeping their kidneys.The Lead Hypothesis2019-07-20T00:00:00+00:002019-07-20T00:00:00+00:00https://danwahl.net/blog/lead-hypothesis<p><strong>Note: What follows is the (lightly edited) transcript of a presentation I gave to <a href="https://www.meetup.com/Effective-Altruism-Chicago/events/262158517/">Effective Altruism Chicago</a> earlier this month.</strong></p>
<p>I had my first contact with the lead hypothesis almost three years ago. I was attending a talk by <a href="https://troyhernandez.com/">Troy Hernandez</a>, data scientist and leader of the <a href="https://pilsenperro.org/">Pilsen Environmental Rights and Reform Organization</a> (PERRO), about the health risks associated with Chicago’s <a href="https://www.chicagotribune.com/investigations/ct-lead-water-pipes-funding-20160921-story.html">water main replacement plan</a>. Troy painted a compelling (and frightening) picture of lead poisoning in Pilsen, and detailed the neighborhood’s historical struggles for environmental justice, and I was inspired.</p>
<p>Since then, PERRO and my company <a href="http://justdesign.coop/">JustDesign</a> have collaborated on four lead-rated water filter distributions, and I’ve written a series of blog posts on my work developing a gadget to enable <a href="/pipe-dream">automatic pipe flushing</a>, quantifying the extent of the harm caused by Chicago’s <a href="/chicago-lead">water main replacements</a>, and <a href="/water-main-map">mapping future water main construction</a> projects across the city.</p>
<p>Throughout this process, I’ve been increasingly exposed to the full scale horror of lead poisoning, from attending Troy’s presentation to reading the alarming <a href="https://www.goodreads.com/book/show/30965518-lucifer-curves">Lucifer Curves</a> by Rick Nevin. For an aspiring effective altruist like myself, accepting even a portion of these claims has implications for cause prioritization, but the issue appears to have been neglected in the Effective Altruism (EA) literature thus far.</p>
<p>And so, with this post, I’ll explore lead poisoning through an EA lens, and make an argument for its consideration as a potential cause area.</p>
<!--more-->
<ul id="markdown-toc">
<li><a href="#introduction" id="markdown-toc-introduction">Introduction</a></li>
<li><a href="#background-on-lead" id="markdown-toc-background-on-lead">Background on lead</a> <ul>
<li><a href="#the-clean-room" id="markdown-toc-the-clean-room">The clean room</a></li>
</ul>
</li>
<li><a href="#exposure-risks" id="markdown-toc-exposure-risks">Exposure risks</a> <ul>
<li><a href="#diet" id="markdown-toc-diet">Diet</a></li>
<li><a href="#dust" id="markdown-toc-dust">Dust</a></li>
<li><a href="#soil" id="markdown-toc-soil">Soil</a></li>
<li><a href="#air-and-gasoline" id="markdown-toc-air-and-gasoline">Air and gasoline</a></li>
<li><a href="#paint" id="markdown-toc-paint">Paint</a></li>
<li><a href="#water" id="markdown-toc-water">Water</a> <ul>
<li><a href="#flint" id="markdown-toc-flint">Flint</a></li>
</ul>
</li>
</ul>
</li>
<li><a href="#individual-and-societal-harm" id="markdown-toc-individual-and-societal-harm">Individual and societal harm</a> <ul>
<li><a href="#individual-effects" id="markdown-toc-individual-effects">Individual effects</a></li>
<li><a href="#violent-crime" id="markdown-toc-violent-crime">Violent crime</a></li>
<li><a href="#unwanted-pregnancy-and-single-parents" id="markdown-toc-unwanted-pregnancy-and-single-parents">Unwanted pregnancy and single parents</a></li>
<li><a href="#inequality-race-iq-and-the-bell-curve" id="markdown-toc-inequality-race-iq-and-the-bell-curve">Inequality, race, IQ, and The Bell Curve</a></li>
</ul>
</li>
<li><a href="#interventions-and-cost-effectiveness" id="markdown-toc-interventions-and-cost-effectiveness">Interventions and cost effectiveness</a> <ul>
<li><a href="#early-interventions" id="markdown-toc-early-interventions">Early interventions</a></li>
<li><a href="#window-replacement" id="markdown-toc-window-replacement">Window replacement</a></li>
<li><a href="#water-filter-distribution" id="markdown-toc-water-filter-distribution">Water filter distribution</a></li>
</ul>
</li>
<li><a href="#conclusion" id="markdown-toc-conclusion">Conclusion</a></li>
<li><a href="#organizations" id="markdown-toc-organizations">Organizations</a></li>
</ul>
<h1 id="introduction">Introduction</h1>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-005.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-005.png" alt="lead-hypothesis-005.png" title="lead-hypothesis-005" class="center-image" /></a></p>
<p>This graph is from a paper titled <a href="https://www.ncbi.nlm.nih.gov/pubmed/22484219">“The urban rise and fall of air lead (Pb) and the latent surge and retreat of societal violence”</a>. I work part time in finance, doing data analysis for a hedge fund. I see a lot of big data sets, and it’s my job to mine small time-series correlations from them to inform novel trading strategies. I’m used to seeing data that is one step away from random noise, and so it’s hard to do justice to how impressive a graph like this is. When I first saw it, I couldn’t believe that a correlation (note, not necessarily causation!) like this existed, and that I didn’t know about it.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-006.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-006.png" alt="lead-hypothesis-006.png" title="lead-hypothesis-006" class="center-image" /></a></p>
<p>Correlation is a tricky thing, though. We live in a world of data, and spurious correlations abound, including this classic (whether or not Nicolas Cage should be an EA cause priority is a topic for another post):</p>
<p>Also problematic is the fact that, as a non-expert, I am going to make some equivalently wild claims, implying that lead poisoning is primarily, causally responsible for some of the most harmful societal trends of the 20th century. Should you believe me?</p>
<p>While working on this post, I’ve been following the recent Slate Star Codex series on <a href="https://slatestarcodex.com/2019/06/03/repost-epistemic-learned-helplessness/">Epistemic Learned Helplessness</a>. Here’s a relevant quote from one of the articles, talking about Immanuel Velikovsky’s <a href="https://www.goodreads.com/book/show/632272.Ages_in_Chaos">Ages in Chaos</a>:</p>
<blockquote>
<p>I read it and it seemed so obviously correct, so perfect, that I could barely bring myself to bother to search out rebuttals. And then I read the rebuttals, and they were so obviously correct, so devastating, that I couldn’t believe I had ever been so dumb as to believe Velikovsky. And then I read the rebuttals to the rebuttals, and they were so obviously correct that I felt silly for ever doubting.</p>
</blockquote>
<p>Scott goes on to say that ignoring arguments “is the correct Bayesian action: if I know that a false argument sounds just as convincing as a true argument, argument convincingness provides no evidence either way. I should ignore it and stick with my prior.” This is something I tried to keep in mind when putting together this post, and I encourage you to do the same.</p>
<p>Clearly it’s going to take more than just arguments to establish that the above graph is <a href="https://xkcd.com/552/">causal, not merely a terrifying coincidence</a>. Since the use of randomized control trials, the gold standard in scientific literature, is obviously unethical in the context of childhood lead poisoning, we’ll have to ascertain implicit causation via other means. Along those lines, here are some questions to consider as we explore the evidence:</p>
<ul>
<li>Does exposure to environmental lead cause measurable biological damage?</li>
<li>Is there a dose-response relationship?</li>
<li>Does this biological damage plausibly lead to negative individual or societal outcomes?</li>
<li>Does more damage lead to even worse outcomes?</li>
<li>Are there natural experiments happening due to varying regulations in different locations?</li>
<li>Do interventions lead to a reduction in biological damage?</li>
<li>Does reduced damage lead to fewer negative outcomes?</li>
</ul>
<p>As you see more graphs like the above throughout this post, try to keep these questions in mind, and we’ll review the evidence for and against each at the end.</p>
<h1 id="background-on-lead">Background on lead</h1>
<p>But before trying to answer those questions directly, let’s start with some basic background information:</p>
<ul>
<li><a href="https://en.wikipedia.org/wiki/Lead">Lead is an element</a>, with atomic number 82.</li>
<li>It sits next to titanium and bismuth on the periodic table.</li>
<li>There is <em>lots of it</em> in the Earth’s crust, especially for its large atomic weight.</li>
<li>Most lead on earth was initially buried, but (unfortunately) it’s easily extracted from ore.
<ul>
<li><a href="https://en.wikipedia.org/wiki/Galena">Galena</a>, which also contains silver, is the most important sources.</li>
</ul>
</li>
<li>Lead has several notable characteristics. It is:
<ul>
<li>relatively inert, and can be used in the presence of reactive chemicals,</li>
<li>high density, making it useful for radiation shielding, from X-Rays to Chernobyl,</li>
<li>remarkably ductile, which comes in handy for plumbing,</li>
<li>and has a low melting point, important for soldering applications.</li>
</ul>
</li>
</ul>
<p>Because of this unique combination of characteristics, lead has been present throughout human history, perhaps most notoriously in Rome, where it was used to line the aqueducts, baths, vats and cooking pots, and even as a sweetener for wine. Even the periodic symbol Pb even comes from the Latin word for lead, plumbum, and so it’s unsurprising that Romans were the first to correlate lead with its harmful side-effects in the second century BCE (though if it’s unlikely to have been a proximal cause behind the fall).</p>
<p>Lead continued to find applications throughout the middle ages, including alchemy, wine preservation, the printing press, and bullets (the combination of which makes the middle ages sound kind of cool?), but wasn’t until the Industrial Revolution when it again reached Roman-levels of production, with the invention of lead paint.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-014.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-014.png" alt="lead-hypothesis-014.png" title="lead-hypothesis-014" class="center-image" /></a></p>
<p>The 20th century gave rise to modern uses of lead beyond plumbing and paint, most notably in the form of tetraethyllead (TEL), marketed as an antiknock additive for gasoline. The proliferation of leaded gasoline provides an interesting anecdote, worthy of a quick diversion.</p>
<h2 id="the-clean-room">The clean room</h2>
<p>Like any good story, the history of lead poisoning is replete with heroes and villains (though unfortunately there are more of the latter). <a href="https://en.wikipedia.org/wiki/Clair_Cameron_Patterson">Clair Patterson</a> is its first true hero, right down to his unlikely origin story.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-015.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-015.png" alt="lead-hypothesis-015.png" title="lead-hypothesis-015" class="center-image" /></a></p>
<p>After graduating from the University of Iowa with M.A. in molecular spectroscopy, and working on the Manhattan Project in WWII, Patterson joined the University of Chicago’s geochemistry program under renowned scientist Harrison Brown. For his dissertation project, Brown directed him to use mass spectrometry and <a href="https://en.wikipedia.org/wiki/Uranium%E2%80%93lead_dating">uranium dating</a> to try to form a more accurate estimate of the age of the earth.</p>
<p>Uranium has a unique half life (the time during which half of an original substance will disappear, in this case forming constituent components like lead) of 4.5 billion years, making it ideal for studying events on a geological timescale. When the solar system formed, the entire quantity of nearby Uranium-238 was simultaneously created, and has been slowly transforming into lead ever since.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-016.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-016.png" alt="lead-hypothesis-016.png" title="lead-hypothesis-016" class="center-image" /></a></p>
<p>Although much of the original surface of the earth has been buried due to plate tectonics, meteorites (such as the one that impacted in Canyon Diablo in Arizona, pictured above) still contain undisturbed samples of this original uranium within <a href="https://en.wikipedia.org/wiki/Zircon">zircon crystals</a>. Because these crystals strongly reject lead when forming, knowing the current ratio of uranium to lead atoms is enough to probabilistically determine the age of the earth.</p>
<p>This was Patterson’s task, though as he quickly discovered, it was not an easy one. While trying to calibrate his system against previously dated zircon crystals, he measured lead concentrations that were orders of magnitude higher than expected. After laboriously confirming these results, Patterson began to suspect that atmospheric lead was contaminating his samples, and developed sterilization procedures that would result in the first true “clean room” facility.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-017.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-017.png" alt="lead-hypothesis-017.png" title="lead-hypothesis-017" class="center-image" /></a></p>
<p>What Patterson didn’t know at the time was that the concentration of atmospheric lead was at its highest point in human history, and still rising, primarily due to the work of (villain #1) <a href="https://en.wikipedia.org/wiki/Charles_F._Kettering">Charles Kettering</a>.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-018.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-018.png" alt="lead-hypothesis-018.png" title="lead-hypothesis-018" class="center-image" /></a></p>
<p>Kettering, a brilliant inventor and the honorary namesake of <a href="https://www.kettering.edu/">Kettering University</a> in Michigan, has the unfortunate distinction of having invented some of the most harmful technologies in the 20th century, including Freon (the CFC most responsible for the early-90s hole in the ozone layer), the Kettering Bug (the first aerial missile, which helped spur the development of guided missiles), and most relevant to Patterson’s work, the TEL antiknock additive, which had become ubiquitous since its release the 1920s.</p>
<p>Patterson’s research was eventually successful, but only after he moved from Chicago to Cal Tech in Pasadena, where he was given the opportunity to build a truly isolated laboratory environment for his measurements. By this time, however, he had grown so concerned about atmospheric lead that he would spend the remainder of his career attempting to bring awareness to the issue.</p>
<p>It was at about the same time that the first modern reports of lead poisoning were getting widespread attention. Workers in TEL plants across the country were developing similar forms of mental illness, or even dying in some cases, presumably from exposure to lead in high concentrations. Given his experience with ubiquitous lead contamination, Patterson was troubled by the implications, but like most of his contemporaries, he assumed that there was some baseline, safe level of lead in the atmosphere prior to the spread of TEL.</p>
<p>However, as he began measuring lead levels in far flung locations, from the bottom of the ocean, to ice sheets in Greenland and Antarctica, he discovered that the further he got from modern civilization, the lower the concentration of atmospheric levels, approaching an apparent baseline of nearly zero. To Patterson this discovery implied the potential for mass poisoning on an unprecedented scale.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-019.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-019.png" alt="lead-hypothesis-019.png" title="lead-hypothesis-019" class="center-image" /></a></p>
<p>His attempts to alert policymakers were predictably opposed by the oil industry, who, after unsuccessfully attempting to get him fired, instead hired <a href="https://en.wikipedia.org/wiki/Robert_A._Kehoe">Robert Kehoe</a> (villain #2, pictured above) to serve as their own scientific expert. Kehoe notoriously used questionable scientific research methods to advocate for lead’s safety, and confidently dismissed Patterson’s claims <a href="http://mentalfloss.com/article/94569/clair-patterson-scientist-who-determined-age-earth-and-then-saved-it">“laughable”</a>.</p>
<p>Kehoe and Patterson would have a famous showdown in front a Senate Committee chaired by Maine Senator Edmund Muskie, an early champion of environmental causes, with Patterson using his research to presciently contradict Kehoe’s claim that there was a safe threshold for lead exposure. Though his advocacy didn’t immediately have the desired effect, Patterson is credited in large part with the EPA regulations that would finally phase TEL out of gasoline by the end of 1995.</p>
<p>Childhood blood lead levels (BLLs) increased dramatically in the early 20th century, primarily due to TEL emissions, but other factors contributed as well.</p>
<h1 id="exposure-risks">Exposure risks</h1>
<p>Although many of the worst contaminates are no longer actively produced, the CDC estimates that 1.5 million children are exposed to harmful levels of lead each year. This is not surprising, given that the United States, despite the known health risks, still consumes more than a million of tons of lead annually. When a child presents with elevated BLLs, the CDC recommends that health care practitioners work to identify all possible lead sources in their environment, and what follows is a summary of the <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2569084/">most common pathways</a> to exposure.</p>
<h2 id="diet">Diet</h2>
<p>Perhaps the most direct pathway is through the unintentional consumption of lead in everyday food items, which accounts for up to 4 µg per day on average. Lead is found in many typical foods, but most prominently in chocolate, for manufacturing reasons that go beyond a single source. More bad news for people who enjoy sweets: candy, especially products imported from Mexico, have been repeatedly found to contain high lead levels, typically due to wrappers printed with lead ink.</p>
<p>Lead is also passed to food and drink through the use of kitchen products, such as leaded crystal (which can release high amounts of lead even when in contact with a liquid for a short time), or ceramic plates and bowls containing lead glaze. Even plastic bread bags have been found to <a href="https://www.ncbi.nlm.nih.gov/pubmed/2029047">contain lead ink</a>, which can leach if the bags are reused for storage of acidic foods.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-023.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-023.png" alt="lead-hypothesis-023.png" title="lead-hypothesis-023" class="center-image" /></a></p>
<p>Lead is <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1695147/">ubiquitous in dietary supplements</a>, with one analysis finding close to 25% contain more than the recommended daily safe dietary intake. This made <a href="https://www.consumerreports.org/vitamins-supplements/lead-poisoning-from-dietary-supplements/">local news in 2016</a>, when the supplement DHZC-2 was implicated in the death of two adults and the poisoning of two children in Chicago.</p>
<p>Finally, lead in breast milk, which is correlated with the mother’s current BLL and history of exposure, can be passed to infants during periods of critical development. Thankfully, <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2566736/">calcium supplementation has been shown to reduce lead in breast milk</a>, at least for women with low dietary calcium.</p>
<h2 id="dust">Dust</h2>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-025.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-025.png" alt="lead-hypothesis-025.png" title="lead-hypothesis-025" class="center-image" /></a></p>
<p>Although dust is typically a secondary source, it represents the main vector by which young children become lead poisoned, and dust levels in a home are the best predictor of childhood BLL. This is primarily due to infant hand-to-mouth behaviors, which cause the ingestion of lead dust from a variety of sources.</p>
<p>Besides dust from paint chips and contaminated soil, other sources include the salts used to stabilize PVCs, found in vinyl miniblinds and artificial Christmas trees, both of which degrade and form dangerous levels of lead dust through years of normal use. Also notable is the rubber infill in <a href="https://www.cdc.gov/nceh/lead/tips/artificialturf.htm">artificial turf</a> on athletic fields, which is manufactured from recycled tires sometimes containing lead.</p>
<p>Lead dust in the home may also stem from occupational hazards. A meta-analysis found that children of lead-exposed workers had double the mean BLLs compared to the general population, and were 20 times more likely to suffer BLLs of more than 20 µg/dL. Construction workers, painters, plumbers, electricians, and other laborers may all have significant daily contact with products containing lead.</p>
<h2 id="soil">Soil</h2>
<p>While soil naturally contains only trace amounts of lead, it has collected the historical fallout from nearby pollution, including high-traffic roads and mining/smelting sites. The problem is normally localized, but is widespread enough that, in 2004, soil represented the most common exposure source in Arizona for children with elevated BLLs.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-027.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-027.png" alt="lead-hypothesis-027.png" title="lead-hypothesis-027" class="center-image" /></a></p>
<p>One example frequently cited in the literature is the Robert Taylor Homes, which bordered the Dan Ryan expressway, Chicago’s main traffic corridor. Soil found in similar high-traffic, dense urban housing can average up to can average 1000 µg/g, enough to raise BLLs 5 µg/dL.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-028.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-028.png" alt="lead-hypothesis-028.png" title="lead-hypothesis-028" class="center-image" /></a></p>
<p>Smelting sites can also put children at risk of elevated BLLs through soil contamination, even up to 20 years after they are closed. In another example from Chicago’s south side, <a href="https://pilsenperro.org/pilsen-soil-project-h-kramer-co/">activism from PERRO</a> led the EPA to hold smelter H. Kramer & Co. responsible for the cleanup of dozens lead-contaminated properties in the surrounding residential area.</p>
<h2 id="air-and-gasoline">Air and gasoline</h2>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-029.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-029.png" alt="lead-hypothesis-029.png" title="lead-hypothesis-029" class="center-image" /></a></p>
<p>From its invention in the 1920s to its eventual phase-out in the mid 90s, TEL represented the most significant source of airbonre lead contamination in the US, but it was not just a domestic problem. As Nevin states in his in his 2007 paper “Understanding international crime trends: The legacy of preschool lead exposure”:</p>
<blockquote>
<p>National trends in average blood lead and the use of lead in gasoline were highly correlated, with median R<sup>2</sup> of 0.94 in Greece, Spain, South Africa, Venezuela, Belgium, Sweden, Mexico, Finland, Canada, New Zealand, Italy, Switzerland, Britain and the USA.</p>
</blockquote>
<p>Since TEL was banned, industrial sources have taken its place as the leading source of airbonre lead, accounting for nearly 80% of total emissions. Modeling by the EPA suggests that local industry is highly correlated with the BLLs of surrounding children, and after decades of decline, mean US air lead levels actually rose in the mid 2000s from industrial emissions.</p>
<p>Other sources of emissions include airplanes using leaded “avgas”, which have been shown to increase lead concentrations in the areas surrounding certain airports, and the demolition of old buildings, which can temporarily increase airbonre lead even as they remove exposure risks due to lead paint.</p>
<h2 id="paint">Paint</h2>
<p>Aside from the middle 20th century (when TEL accounted for the largest exposure risk), lead paint has been the primary modern source for childhood lead poisoning. From its invention in the late 1800s up through a 1978 ban, white lead paint was a popular option for interior surfaces, consuming almost one third of total US lead production in the early 20th century. Worse yet, marketing campaigns often directly targeted children’s rooms, using characters like the Dutch Boy and his “lead family,” long after the safety hazards were known:</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-032.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-032.png" alt="lead-hypothesis-032.png" title="lead-hypothesis-032" class="center-image" /></a></p>
<p>Exposure risks from lead paint persist today, with up to 38 million homes still containing lead paint as of 2000, 24 million of which were in a deteriorating condition. This is why lead paint and corresponding dust still account for up to 70% of elevated blood lead levels, including more than 5% of all preschool children living in housing built before 1950 (compared to just 0.4% in housing built after 1977).</p>
<p>While all housing built before the ban on lead paint should be considered a significant hazard, the distribution is uneven. Families with incomes below the poverty level, and especially those in the Northeast and Midwest (where white lead paint was most popular) are especially vulnerable, experiencing almost double the exposure risk.</p>
<p>One interesting exception is New York City, where an extensive slum clearance and an early lead paint ban in the 1960s resulted in significantly reduced BLLs:</p>
<blockquote>
<p>Chicago, Detroit, Baltimore, Philadelphia, and St. Louis report 3–4% of children tested in 1998–1999 had blood lead over 20 µg/dL, but New York City prevalence over 20 µg/dL was just 0.4%.</p>
</blockquote>
<h2 id="water">Water</h2>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-034.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-034.png" alt="lead-hypothesis-034.png" title="lead-hypothesis-034" class="center-image" /></a></p>
<p>Lead in the water supply has been a known health risk since the Roman Empire, but unlike paint and gasoline (where technological innovation has supplied lead-free alternatives), plumbing is still inextricably tied to its namesake, and no federal ban on the use of lead pipes has yet been enacted.</p>
<p>Instead, a measurement-based approach is used to determine the lead hazard in the water supply, governed by EPA’s <a href="https://en.wikipedia.org/wiki/Lead_and_Copper_Rule">Lead and Copper Rule</a> (LCR). This is a particularly bad approach for a number of reasons:</p>
<ol>
<li>The LCR mandates that at least 50 samples of tap water be measured at various locations around a municipality every three years, but <a href="https://www.theguardian.com/us-news/2016/feb/19/chicago-water-department-testing-lead-flint-michigan">does not adequately specify the distribution</a> of these measurements, creating a perverse incentive for local authorities to concentrate sampling in areas where water quality is known to be below the 15 ppb limit.</li>
<li>The 15 ppb limit itself is <em>not</em> a safe threshold, but is instead a “reasonable” target based on the number of municipalities that are likely to fail testing, and the EPA’s capacity to monitor their progress at current funding levels. Water levels in excess of 15 ppb are associated with a 14% increase in children with BLLs above 10 µg/dL.</li>
<li>Water testing is an inherently probabilistic, and the <a href="https://www.epa.gov/sites/production/files/2016-02/documents/epa_lcr_sampling_memorandum_dated_february_29_2016_508.pdf">recommended sampling procedure</a> is likely to produce a fat-tailed distribution, since lead pipes flake off in chunks instead of continuously.</li>
</ol>
<p>A driving factor here is cost. In Chicago alone, it would take an estimated <a href="https://news.wttw.com/2018/10/29/aldermen-consider-2-billion-plan-get-lead-out-city-water">$2 billon dollar investment</a> to replace all the current LSLs. However, other cities like Milwaukee and Pittsburgh have devised innovative cost sharing solutions, sometimes even mandating that LSLs be replaced during adjacent construction such as water main repair.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-036.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-036.png" alt="lead-hypothesis-036.png" title="lead-hypothesis-036" class="center-image" /></a></p>
<p>As childhood lead exposure decreases from other sources like gasoline and paint, water becomes a more important exposure risk. The crisis in Flint is an interesting case study on the lurking dangers of LSLs, and a few years ago I had the opportunity to interview Laura Sullivan, (ironically) a professor at Kettering University and <a href="https://news.kettering.edu/news/kettering-university-faculty-member-playing-leading-role-helping-flint-solve-water-issues">appointee to the Flint Water Inter-Agency Coordinating Committee</a>.</p>
<h3 id="flint">Flint</h3>
<p>Much like Chicago and other Midwestern cities, Flint was built during the peak use of LSLs, but up until 2013, the water supply was “well maintained” through the use of chemical additives like orthophosophate, a corrosion inhibitor which works by creating a protective film on the interior of lead pipes. Flint had a longstanding agreement to purchase this treated water from Detroit, but in the aftermath of the financial crisis, the two cities (led by governor appointed, interim emergency managers), could not come to an agreement over the continued use of Detroit’s supply.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-037.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-037.png" alt="lead-hypothesis-037.png" title="lead-hypothesis-037" class="center-image" /></a></p>
<p>Agitated by local business interests, Flint instead opted to join with bordering Genesee County in the formation of the Karegnondi Water Authority (KWA), which planned to build a new pipeline directly from Lake Huron to the surrounding area. One caveat to this deal was that, because a small section of the current pipeline from Detroit to Flint was to be reused by KWA, Flint would have to find an alternative water source for several years until construction was completed. In a fateful decision with lethal consequences, Flint management decided to tap local river water to meet this interim need.</p>
<p>Although Flint’s treatment plant had been “finishing” water imported from Detroit for decades, it was wholly unprepared to deal with the corrosive nature of the Flint river water. Beyond the damage to the lead pipes, which (hero #2) local pediatrician Mona Hanna-Attisha correlated with elevated BLLs in children across the city, the varied use of chlorine combined with the proliferation of abandoned homes led to an outbreak of Legionnaires disease, sickening 90 and killing a dozen.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-039.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-039.png" alt="lead-hypothesis-039.png" title="lead-hypothesis-039" class="center-image" /></a></p>
<p>Eventually Flint’s contaminated water supply attracted widespread media attention, thanks in no small part to the advocacy of local mother LeAnne Walters and Chicago-based EPA employee Miguel Del Toral (heroes #3 and #4), another hero of our story, who will feature again later in this post. But Flint <em>still</em> does not have access to safe drinking water five years later, and should serve as a cautionary tale for other cities like Chicago about the present danger of LSLs.</p>
<h1 id="individual-and-societal-harm">Individual and societal harm</h1>
<p>While environmental lead levels have been declining since the mid-20th century, exposure risks still abound, especially for children and vulnerable populations. Exactly how bad is that?</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-041.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-041.png" alt="lead-hypothesis-041.png" title="lead-hypothesis-041" class="center-image" /></a></p>
<p>Lead is a known neurotoxin, and has cascading negative effects that impact both individual and societal health. Much of what we’ll discuss relates to “elevated” BLLs of 20 µg/dL or above, but studies have demonstrated long-lasting effects at levels as low as 1/5th that amount, and (as of 2012) the CDC <a href="https://www.cdc.gov/nceh/lead/default.htm">explicitly states</a> that “no safe blood lead level in children has been identified.”</p>
<h2 id="individual-effects">Individual effects</h2>
<p>Although, as the plight of workers in TEL plants demonstrates, lead exposure can harm adults, much of the research focuses on children and expectant mothers. There are several reasons for this, but they all revolve around a set of common themes.</p>
<p>First, children absorb lead more readily than adults, in part because they are growing at a faster rate, but also because of the way lead interacts the body on a cellular level. Our cells require minerals such as calcium, zinc, and iron to function properly, and lead interrupts this process by mimicking these metals, eventually leading to cellular death.</p>
<p>Second, children are more likely to be unintentionally exposed to household lead dust, especially around the age of 15-24 months, due to normal hand-to-mouth behaviors. As we discussed above, lead dust has a variety of sources, including paint, soil, and parental occupational exposure.</p>
<p>Finally, evidence suggests that lead exposure may cause permanent cognitive impairment during the critical stages of neural growth before the age of three. Symptoms of this damage include incomplete development of the blood brain barrier, and the destruction of frontal lobe myelin sheaths, which insulate white matter connections.</p>
<p>The correlation between increased BLLs and IQ loss (presumably as a result of this neurological damage) has been extensively studied, with different analyses producing results similar this graph from Lucifer Curves:</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-042.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-042.png" alt="lead-hypothesis-042.png" title="lead-hypothesis-042" class="center-image" /></a></p>
<p>Especially noteworthy is the steep decline in IQ at low levels of exposure, which is critical to the importance of future interventions that target relatively small exposure risks.</p>
<p>These neural impairments also appear to have negative effects that go beyond just measured IQ, as behavioral problems throughout young adulthood, including ADHD diagnoses, are strongly correlated childhood lead exposure. While some studies describe these issues as an indirect effect of IQ loss (noting that low IQ youths are up to seven times more likely to be incarcerated than those with a high IQ), at least one study directly implicates the disruption of myelin formation with reduced impulse control in teens.</p>
<p>But to avoid painting too bleak a picture, it’s important to note that, while lead exposure can cause permanent damage, neurological effects may be reversible absent continuous exposure, as we’ll see in the next section on interventions. For now, Nevin reminds us that “in 1976-1980, 99.8% of all children ages 1-5 had blood lead above 5 µg/dL (and 88.2% had blood lead above 10 µg/dL)”, and the majority of them turned out just fine.</p>
<h2 id="violent-crime">Violent crime</h2>
<p>Even at a low risk of individual harm, however, the cumulative damage of lead poisoning has been enough to leave its mark on society as a whole.</p>
<div class="alert alert-warning" role="alert">
<p class="h6 text-muted">Author's note:</p>
<p>The following several sections are based on Rick Nevin's work, which culminated in the publication of Lucifer Curves in 2016. His papers are as alarming as they are dense with citations, and don't lend themselves well to summary, but I'll do my best.</p>
<p>Also, in anticipation of any single-source criticism, see <a href="https://www.ncbi.nlm.nih.gov/pubmed/11343501">Stretesky and Lynch (2001)</a> and <a href="https://www.biologicaldiversity.org/campaigns/get_the_lead_out/pdfs/health/Needleman_2004.pdf">Needleman et al. (2003)</a> for two independent works that arrive at essentially the same conclusion.</p>
</div>
<p>In two seminal papers from the turn of the century, economist Rick Nevin sought to answer what was a perplexing question at the time. In the mid-90s, researchers at the US Bureau of Justice Statistics had projected an increase in teen murders (based on population grown and changes in demographics), but instead observed a historic 77% fall in the juvenile murder arrest rate through 2003. Moreover:</p>
<blockquote>
<p>In 1991, the violent crime arrest rate for ages 10-14 was 2.3 times higher than the arrest rate for ages 50-54, but in 2014 the arrest rate for ages 10-14 was 35% lower than the arrest rate for ages 50-54. If brain function affects crime, then arrest rate trends by age suggest that brain function from 1991 to 2014 improved for youths but deteriorated for older adults. Why?</p>
</blockquote>
<p>Nevin proposed a novel answer–the time-lagged effects of childhood lead poisoning–and he had the data to back it up.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-045.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-045.png" alt="lead-hypothesis-045.png" title="lead-hypothesis-045" class="center-image" /></a></p>
<p>First, he demonstrated that the variables of interest were correlated. For childhood lead poisoning to be a plausible causal factor in future violent crimes, it’s necessary to show both the relationship between lead exposure and BLLs, and BLLs and violent crime. The graphs above and below demonstrate these two relationships, and the underlying data suggest that 90% of the variance of 1964-1998 violent crime in the USA can be explained by gasoline emissions between 1941-1975. As an example of this trend, Nevin explains that:</p>
<blockquote>
<p>Youths ages 16–22 in 1994 were all born before the early-1980s fall in gasoline lead, and the age-16 arrest rate was 29% higher than the age-22 rate in 1994, consistent with criminal behavior being moderated by changes in frontal lobe development of adolescents and young adults. The 22-year-olds in 2001 were also born before the early-1980s decline in lead exposure, but the 16-year-olds were born in the mid-1980s, and the 2001 age-16 arrest rate was 12% lower than the age-22 arrest rate.</p>
</blockquote>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-046.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-046.png" alt="lead-hypothesis-046.png" title="lead-hypothesis-046" class="center-image" /></a></p>
<p>Next he extended these results with equivalent international data sets, showing that childhood BLLs tracked concurrent lead emissions across a diverse cross section of countries:</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-048.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-048.png" alt="lead-hypothesis-048.png" title="lead-hypothesis-048" class="center-image" /></a></p>
<p>And also that index crime rates (as separately defined in each country) were strongly correlated with childhood BLLs at a 19 year lag:</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-049.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-049.png" alt="lead-hypothesis-049.png" title="lead-hypothesis-049" class="center-image" /></a></p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-050.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-050.png" alt="lead-hypothesis-050.png" title="lead-hypothesis-050" class="center-image" /></a></p>
<p>What’s especially noteworthy about these results is that the various technological expansions and regulatory restrictions happened at different times in each country, yet the underlying relationship was common throughout! Nevin gives an example highlighting this disparity:</p>
<blockquote>
<p>The very high significance of blood lead at lags consistent with peak offending ages is especially striking in light of divergent crime rate trends. Canada’s index crime rate was 60% higher than the rate in Britain in the early-1970s, but 20% lower in 2001.</p>
</blockquote>
<p>Aside from the violent crime index in each country, Nevin also investigated burglary, robbery, and assault, concluding that “international crime trends are inconsistent with theoretical effects of police per capita, incarceration, and demographic trends,” but all results were consistent with lead poisoning as a causal factor. From this, he extrapolates:</p>
<blockquote>
<p>The high R<sup>2</sup> (63–93%) in each single-nation index crime regression with a 19-year lag also suggests that blood lead affects many types of criminal behavior including simple assaults and petty thefts.</p>
</blockquote>
<p>Which is consistent with the expected repression of impulse control caused by neurological damage to myelin sheaths.</p>
<p>If you read enough of Nevin’s work, you begin to notice that he has a somewhat cavalier attitude when it comes time-series regressions. A proper analysis should probably have used a pre-registered value for the expected time lag, or split the country data into separate training and testing datasets, but instead Nevin followed a less rigorous procedure:</p>
<blockquote>
<p>Single and combined nation regressions were run with 5–45 year lags to identify “best-fit” lags for each crime, with the highest significance (t-value) for blood lead and percent of crime rate variation explained (R<sup>2</sup> ).</p>
</blockquote>
<p>In other words, Nevin ran 41 experiments and presented the best results, or the same basic procedure responsible for <a href="https://en.wikipedia.org/wiki/Data_dredging">p-hacking</a> and the <a href="https://en.wikipedia.org/wiki/Replication_crisis">replication crisis</a> in psychology. However, he makes a compelling argument that picking the optimal time lag is not essential for his conclusion, sharing the following graph demonstrating the variability of the international trends in each crime category:</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-054.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-054.png" alt="lead-hypothesis-054.png" title="lead-hypothesis-054" class="center-image" /></a></p>
<p>He also directly addresses the possibility of spurious correlations, stating:</p>
<blockquote>
<p>Although time series comparisons can result in coincidental correlations, no nation shows any correlation between burglary and blood lead at lags of less than 10 or over 38 years.</p>
</blockquote>
<p>Taken as a whole, Nevin’s work strongly implies that either childhood lead poisoning or an equally powerful (and important!) set of confounders is primarily responsible for the bulk of crime in young adulthood.</p>
<h2 id="unwanted-pregnancy-and-single-parents">Unwanted pregnancy and single parents</h2>
<p>Nevin casually proceeds to use his lead hypothesis to tackle several additional, controversial social issues, beginning with abortion. Freakonomics authors Steven Levitt and Stephen J. Dubner famously claimed that the decline in crime in the 1990s was best explained by newly-legal abortion of unwanted pregnancies in the late 60s (as well as increasing incarceration rates).</p>
<p>Predictably, Nevin rejects this claim, providing evidence that declining crime in New York State did not track abortion legalization except in New York City, which experienced, you guessed it, a simultaneous decline in lead poisoning due to aforementioned slum clearance and a lead paint ban in the 60s. He also points out that:</p>
<blockquote>
<p>Britain legalized abortion before the USA, but violent crime rose in Britain and across Europe and Oceana in the 1990s despite rising incarceration rates.</p>
</blockquote>
<p>Next he turns his attention to single mothers, rejecting the conventional wisdom that the decline of two parent households was a causal factor for rising juvenile crime in the latter half of the 20th century:</p>
<blockquote>
<p>That rise in juvenile offending coincided with a 1960s rise in the unwed teen birth rate, and the 1990s decline in juvenile arrests coincided with a falling unwed teen birth rate. Higher offending due to single parents would be consistent with juvenile offending that lagged the unwed birth trend by 12–17 years, as children raised by single mothers became teenagers. The coincident rise and fall of unwed birth rates and juvenile offending is inconsistent with the time precedence indicator of causation.</p>
</blockquote>
<p>The evidence, as Nevin argues, suggests a different explanation for these societal trends:</p>
<blockquote>
<p>Blood lead prevalence over 30 µg/dL among white USA children fell from 2% in 1976–1980 to less than 0.5% in 1988–1991, as prevalence over 30 µg/dL among black children plummeted from 12% to below 1%. The white juvenile murder arrest rate then fell from 6.4 to 2.1 from 1993–2003, as the black juvenile rate fell from 58.6 to 9.7. That 83% fall in the black juvenile murder arrest rate occurred with just 36% of black children living in two-parent families in 1993, and in 2003.</p>
</blockquote>
<h2 id="inequality-race-iq-and-the-bell-curve">Inequality, race, IQ, and The Bell Curve</h2>
<p>Finally, Nevin extends his hypothesis to dispute the findings of perhaps the most notorious book of the preceding decade: The Bell Curve. Published in 1994, authors Richard Herrnstein and Charles Murray analyzed IQ score distributions across various biological and social factors, concluding that there were meaningful genetic differences in intelligence between races.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-059.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-059.png" alt="lead-hypothesis-059.png" title="lead-hypothesis-059" class="center-image" /></a></p>
<p>Nevin analyzed the same data, but arrived at a very different conclusion. He began by showing that, because of the prominence of airborne lead contamination in the 1950s and 60s (affecting the same cohort studied in The Bell Curve), children living in city areas with urban traffic congestion experienced greater levels of lead exposure, since the majority of the lead dust settles within approximately 10 miles of auto emissions.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-060.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-060.png" alt="lead-hypothesis-060.png" title="lead-hypothesis-060" class="center-image" /></a></p>
<p>He again gives the example of the Robert Taylor Homes in Chicago, which bordered the Dan Ryan expressway, and absorbed the fallout from 150,000 vehicles/day worth of lead exhaust. Nearly two decades after they opened, the predominantly minority residents experienced 11% of Chicago’s total murders, despite only accounting for 0.5% of the total population. From this and other examples, Nevin infers that:</p>
<blockquote>
<p>Preschool lead exposure is highly correlated with social factors because poor children are more likely to live in older housing with deteriorated paint, and black children were concentrated in cities with higher air lead.</p>
</blockquote>
<p>Not only were children living in cities at risk for higher average BLLs, but 25% of city children screened in 1970 had BLLs in excess of 40 µg/dL. Nevin goes on to observe that this trend continued beyond the scope of data considered in The Bell Curve, drawing from his earlier analysis of violent crime to show that:</p>
<blockquote>
<p>A stronger association between severe lead poisoning and violence is also consistent with racial differences in late-1970s blood lead and early-1990s juvenile arrest rates. Average 1976–1980 blood lead for black children ages 6–36 months was 50% above the average for white children, but blacks were six times more likely to have blood lead of 30–39 µg/dL and eight times more likely to be over 40 µg/dL.</p>
</blockquote>
<p>Nevin concludes that racial disparities in IQ, which The Bell Curve attributes in part to genetic predisposition, can be better explained by housing inequality, and the corresponding differences in exposure to environmental lead.</p>
<p>Inequitable environmental factors persist even today, with children living in the 10 largest US cities in the early 2000s accounting for nearly half of elevated blood lead levels nationwide, despite representing less than 10% of the population. And disparity exists even within cities, with 50% of the children with EBLs concentrated in only 11% of local ZIP codes.</p>
<p>But there is also evidence to suggest that these trends are slowly reversing. Even in the middle of national decline in violent crime, the mid-90s ban on leaded gasoline equalized murder rates across all but the smallest cities:</p>
<blockquote>
<p>From 1981–1991, USA murder rates rose 3% in cities of 100–500 thousand, 9% in cities of 500,000 to 1 million, and 26% in cities over a million. The 1980s phase-out of gas lead left little air lead difference by city size, and average 2000–2002 murder rates were 14.7 (per 100,000) in cities over a million, 14.6 in cities of 500,000 to a million, 15.0 in cities of 250–500 thousand, and 9.5 in cities of 100–250 thousand.</p>
</blockquote>
<p>And although publications like The Bell Curve made high-profile speculation about the root cause of racial differences, more recent data show that, while both black and white children have improved across various metrics, black children have closed the gap to such an extent that in 2014 they experienced lower juvenile arrest, dropout, and pre-teen pregnancy rates than their 1994 white peers. This trend is consistent with overall reductions in BLLs, especially in disadvantaged communities.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-064.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-064.png" alt="lead-hypothesis-064.png" title="lead-hypothesis-064" class="center-image" /></a></p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-065.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-065.png" alt="lead-hypothesis-065.png" title="lead-hypothesis-065" class="center-image" /></a></p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-066.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-066.png" alt="lead-hypothesis-066.png" title="lead-hypothesis-066" class="center-image" /></a></p>
<h1 id="interventions-and-cost-effectiveness">Interventions and cost effectiveness</h1>
<p>Childhood lead exposure in the United States has been declining for decades, and is currently at its lowest level since the Industrial Revolution. While we’ve highlighted a few heroes so far, the bulk of the work has been done by a faceless bureaucracy, who have designed and implemented regulations like the ban on leaded gasoline, among “the most important determinants of the decline in crime rates over the past two decades.”</p>
<p>But the work is far from finished. Since the CDC began collecting statistics on BLLs in 1997, they have documented nearly one million children exceeding their action threshold of 10 μg/dL, and a 2004 study estimates that there are another 120 million worldwide, accounting for nearly 1% of the total burden of disease. Worse yet, it’s estimated that nearly 10% of young children worldwide have BLLs exceeding 20 μg/dL, 99% of whom live in developing countries.</p>
<p>Below I’ll summarize three plausible interventions, and report on the cost effectiveness of each. Where possible, I’ll try to compare these with other EA cost estimates, but future research is needed to form a more comprehensive picture.</p>
<h2 id="early-interventions">Early interventions</h2>
<p>The first study comes from the American Economic Journal by Stephen Billings and Kevin T. Schnepel, titled <a href="https://www.aeaweb.org/articles?id=10.1257/app.20160056">“Life after Lead: Effects of Early Interventions for Children Exposed to Lead”</a>. We talked earlier about how, when it comes to lead poisoning, there can be no true randomized control trials, but through clever design and analysis, this study comes about as close as you can get.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-068.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-068.png" alt="lead-hypothesis-068.png" title="lead-hypothesis-068" class="center-image" /></a></p>
<p>The basic premise is that, following CDC guidelines, the city of Charlotte, NC, takes increasingly expansive action as BLLs in individual children exceed thresholds starting at 10 μg/dL in consecutive tests. The goal of treatment “is to prevent further exposure, and to reduce lead levels in affected children,” with practitioners initially providing parents lead education and nutritional counseling.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-069.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-069.png" alt="lead-hypothesis-069.png" title="lead-hypothesis-069" class="center-image" /></a></p>
<p>For their study, Billings and Schnepel noticed that children who had only one elevated blood test, with the second falling just below the threshold, had indistinguishable demographic characteristics as those who received treatment, serving as reasonable control group for their experiment. This is especially clever because, as the authors note, capillary blood tests (often performed on both the first and second round of testing) are notoriously prone to external contamination, making the differences between the treatment and control group plausibly randomized.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-070.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-070.png" alt="lead-hypothesis-070.png" title="lead-hypothesis-070" class="center-image" /></a></p>
<p>They gathered not only blood test data, but also birth certificate information and family history, administrative records from the school district, local criminal arrest records for the same cohort in young adulthood, and even county assessor data on household construction projects. Note that all the children in this study were born after 1990, on the tail end of exposure risks from leaded gasoline, so the results are likely still meaningful today.</p>
<p>The authors found that children in the treatment group experienced an small improvement in educational outcomes:</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-071.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-071.png" alt="lead-hypothesis-071.png" title="lead-hypothesis-071" class="center-image" /></a></p>
<p>And a significant decline in behavioral risks, relative to the control group. These results are consistent with the harms associated with lead poisoning, and especially noteworthy because the treatment group had <em>higher</em> initial BLLs, on average. In fact, the authors were unable to reject the null hypothesis that the resulting behavioral score of the treatment group was different than the children with BLL lower than 5 μg/dL!</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-072.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-072.png" alt="lead-hypothesis-072.png" title="lead-hypothesis-072" class="center-image" /></a></p>
<p>The authors also investigated children in the vicinity of the CDC’s 20 μg/dL threshold, above which an environmental investigation is conducted on the child’s home to identify the source of lead exposure, showing that “that children above 20 μg/dL have significantly better outcomes than those in the 15-20 range.”</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-073.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-073.png" alt="lead-hypothesis-073.png" title="lead-hypothesis-073" class="center-image" /></a></p>
<p>Although this study only measured intent to treat, and was unable to determine if any actions were taken beyond the CDC recommendations, construction records suggest that there were, with parcels that received lead remediation more than three times as likely to be in the treatment as the control group. Although the sample size is small, remediation appears to have conferred benefits to both the younger siblings at the same address, and also to the young children of future residents. This result is consistent with prior studies, which show that remediation programs significantly reduce household lead dust, and others which find “mean BLLs of children whose housing was abated show a 38% decrease over a 2-year period after lead hazard control.”</p>
<p>Finally, the authors note that:</p>
<blockquote>
<p>we find larger effects for individuals experiencing a significant drop (more than 5 μg/dL) between the second and third BLL test. Individuals who experience a sharp drop in BLLs after two consecutive tests over the alert threshold are more likely to have benefited from a reduction in exposure.</p>
</blockquote>
<p>However, besides the intent to treat problem, there are other caveats to consider. For one, related research shows that children generally benefit from all forms of early health interventions, and the improvement of treatment group across various metrics could plausibly be a result of better nutrition and increased parental attention.</p>
<p>Also, the known inaccuracies in BLL measurements, both related to capillary tests, and the half-life of lead in blood (approximately 30 days), mean there are large uncertainties in the data, dependent on both the method and exact date of testing relative to the exposure.</p>
<p>Still, the authors conclude that there is robust evidence in favor of their hypothesis, and recommend applying similar interventions at lower BLL thresholds. Despite an average cost of nearly $7,500 per home remediation, the expected benefits for this intervention (and similarly effective childhood health programs) are close to $10,000, giving the program an overall benefit-to-cost ratio of about 1.4:1.</p>
<p>Maybe more importantly, this study provides hope that some of the worst effects of childhood lead poisoning may not be permanent, and that (cost) effective interventions, already being practiced across the country, may have the potential for expansion.</p>
<h2 id="window-replacement">Window replacement</h2>
<p>Another paper, titled <a href="https://www.ncbi.nlm.nih.gov/pubmed/17961540">“Monetary Benefits of Preventing Childhood Lead Poisoning With Lead-Safe Window Replacements”</a>, more directly examines the effects of lead remediation. Instead of going through the laborious process of determining which children have elevated BLLs, our old friend Nevin (et. al.) makes the case for just replacing <em>all</em> of the windows in pre-1960s housing.</p>
<p>To justify this considerable expense, they first point out that, since the near elimination of airborne lead associated with gasoline, the historical vestiges of lead paint in the housing infrastructure now represent the lowest hanging fruit for targeted remediation. Moreover, the harm caused by substandard housing disproportionately affects (the typical poorer) children living in pre-1940s housing, who are six times more likely to have elevated blood lead levels than their peers.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-075.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-075.png" alt="lead-hypothesis-075.png" title="lead-hypothesis-075" class="center-image" /></a></p>
<p>This makes sense, because the EPA found lead paint hazards in 68% of pre-1940 homes, compared to just 3% of post-1977 homes.</p>
<p>Lead paint hazards can be reduced via both interim controls and stabilization, or through permanent abatement, but for the purposes of this study, Nevin et. al. defined lead-safe window replacement to be:</p>
<ol>
<li>Replacement of all single-pane windows with high-efficiency ENERGY STAR windows;</li>
<li>Stabilization of any significantly deteriorated paint;</li>
<li>Specialized cleaning to remove any lead-contaminated dust following the repairs; and</li>
<li>Clearance testing (which includes dust wipe analysis) to confirm the absence of lead dust hazards after project cleanup.</li>
</ol>
<p>To calculate the cost effectiveness of window replacement, the authors first converted the benefits of eliminating household lead dust to IQ by estimating the BLL increase associated with varying degrees of contamination, and then the IQ loss associated with this change in BLL.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-077.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-077.png" alt="lead-hypothesis-077.png" title="lead-hypothesis-077" class="center-image" /></a></p>
<p>Next, they used a well-established estimate for the lifetime earnings benefit of each additional IQ point ($16,809), along with a variety of empirical factors related to the hazard prevalence across historical housing construction periods, to find the value associated with each category of remediation.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-078.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-078.png" alt="lead-hypothesis-078.png" title="lead-hypothesis-078" class="center-image" /></a></p>
<p>Nevin et. al. note that this is likely a conservative estimate, because they assumed the benefits of environmental lead reduction would only be realized in children younger than 30 months, and also included a 1/5 factor in the calculation to reflect the probable presence of deteriorated paint.</p>
<p>Finally, they considered the costs of window replacement for a variety of household scenarios, and compared those against the improvement associated with new windows on the household market value, as shown in the figure below.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-079.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-079.png" alt="lead-hypothesis-079.png" title="lead-hypothesis-079" class="center-image" /></a></p>
<p>Putting it all together, and including the energy savings associated with more efficient windows, the authors report a net societal benefit of about $5,000 for each pre-1940s housing unit remediated, or up to $67 billion if applied to the entire stock of pre-1960s housing.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-080.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-080.png" alt="lead-hypothesis-080.png" title="lead-hypothesis-080" class="center-image" /></a></p>
<p>Note that, again, this is likely a conservative estimate. The societal benefit of averting diseases such as ADHD, also correlated with lead poisoning, along with the corresponding reduction in violent crime detailed in Nevin’s other work, might save up to an additional $45 billion dollars per year, even before considering the reduction in greenhouse gas emissions associated with more efficient household heating and air conditioning.</p>
<p>Nevin suggests that a one-time federal expenditure of $22 billion, in the form of $100 credits per window replaced, might be sufficient to incentive much of this change, and would pay for itself in less than a year. He continues:</p>
<blockquote>
<p>We should set a national goal to replace all single-pane windows in pre-1978 housing, targeting pre-1950 housing as the first priority. This goal can be achieved by replicating the proven success of the Illinois Comprehensive Lead Education, Reduction, and Window Replacement (Clear-Win) pilot program.</p>
</blockquote>
<p>That program, which reported on its results in a <a href="https://www.ncbi.nlm.nih.gov/pubmed/26910871">separate 2016 paper</a>, found that window replacement significantly reduced household lead dust, while simultaneously increasing thermal comfort and reducing energy bills, all at a 1.7:1 benefit to cost ratio.</p>
<h2 id="water-filter-distribution">Water filter distribution</h2>
<p>Finally, I’ll quickly summarize some of my earlier work on childhood lead poisoning from drinking water in Chicago, but a more detailed analysis can be found <a href="/chicago-lead">elsewhere</a> on my blog.</p>
<p>Back at the beginning of this post, I mentioned attending a talk by PERRO’s Troy Hernandez, which represented my first exposure to the lead hypothesis back in 2017. What I didn’t say was that, after his speech was finished, I cornered him in the back of the the lecture hall and demand to know what I personally could do to solve the problem.</p>
<p>After I calmed down a bit, Troy and I exchanged information, and that turned out to be the beginning of a partnership between our two organizations that would take in $10K from two separate grants, and provide free lead-rated water filters to nearly 500 Pilsen children under the age of six.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-082.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-082.png" alt="lead-hypothesis-082.png" title="lead-hypothesis-082" class="center-image" /></a></p>
<p>In order to secure these grants, we put together a cost benefit analysis, which I’ll briefly describe below, but should sound somewhat familiar after the previous two summaries.</p>
<p>First, we needed to establish the baseline rate of lead in Chicago’s water supply. As most locals are already aware, if only from the ubiquitous street construction, Chicago is in the middle of a <a href="https://www.nytimes.com/2011/12/18/us/chicago-inaugurates-costly-plan-to-replace-aged-water-mains.html">10 year plan</a> to replace it’s entire aging water main infrastructure. Local EPA employee and Flint hero Miguel Del Toral authored a <a href="https://www.epa.gov/sites/production/files/2015-10/documents/lead-service-lines-study-20130723.pdf">2013 study</a> demonstrating that, among other things, water main replacement can disturb LSLs, and temporarily raise the quantity of lead in tap water.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-083.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-083.png" alt="lead-hypothesis-083.png" title="lead-hypothesis-083" class="center-image" /></a></p>
<p>Like Nevin’s work above, it was then necessary to convert this environmental hazard into BLLs, and for this we used a best fit linear regression of the following table from Bruce Lanphear’s 1998 paper <a href="https://www.ncbi.nlm.nih.gov/pubmed/9515067">“Environmental Exposures to Lead and Urban Children’s Blood Lead Levels”</a>:</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-084.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-084.png" alt="lead-hypothesis-084.png" title="lead-hypothesis-084" class="center-image" /></a></p>
<p>Next, to go from BLL to to IQ loss, we used data from a meta-analysis (again by Lanphear et. al.) titled <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2533151/">“Low-Level Environmental Lead Exposure and Children’s Intellectual Function: An International Pooled Analysis”</a>, which included the following graph (showing the same steep decline in IQ scores at relatively low BLLs as in Nevin’s work):</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-085.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-085.png" alt="lead-hypothesis-085.png" title="lead-hypothesis-085" class="center-image" /></a></p>
<p>Unlike Nevin, who converted IQ loss to a decrease in lifetime earnings potential, we instead used estimates from the Global Burden of Disease to fit a linear regression between IQ sigma value ranges and Disability Adjusted Life Years.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-086.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-086.png" alt="lead-hypothesis-086.png" title="lead-hypothesis-086" class="center-image" /></a></p>
<p>Finally, after much difficulty, we found the an approximate lognormal distribution of current BLL levels in Chicago children by scraping values from several charts included in an online presentation by Chicago’s Medical Director for Environmental Health. (Don’t try this at home.)</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-087.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-087.png" alt="lead-hypothesis-087.png" title="lead-hypothesis-087" class="center-image" /></a></p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-088.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-088.png" alt="lead-hypothesis-088.png" title="lead-hypothesis-088" class="center-image" /></a></p>
<p>With the full suite of data and correlations available, we ran a series of Monte Carlo simulations, generating random cohorts of children representing the likely recipients of our free filter distribution. As summarized on the table below, we found that, for a cost of less than $50/child, we could provide up to four months of lead protection after a nearby water main replacement.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-089.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-089.png" alt="lead-hypothesis-089.png" title="lead-hypothesis-089" class="center-image" /></a></p>
<p>This, in turn works out a median projection of approximately three DALYs per $1000, or about 1/4 the cost effectiveness of GiveWell’s gold standard, the <a href="https://www.givewell.org/charities/amf">Against Malaria Foundation</a>; not too shabby for a local intervention.</p>
<p><a href="/assets/images/lead-hypothesis/lead-hypothesis-090.png"><img src="/assets/images/lead-hypothesis/lead-hypothesis-090.png" alt="lead-hypothesis-090.png" title="lead-hypothesis-090" class="center-image" /></a></p>
<h1 id="conclusion">Conclusion</h1>
<p>Earlier, I posed a set of questions to guide our investigation into a potentially causal link between lead poisoning and negative individual and societal outcomes. Let’s briefly review those questions, and check them against the evidence provided above.</p>
<table>
<thead>
<tr>
<th>Question</th>
<th style="text-align: center">Verdict</th>
<th>Notes</th>
</tr>
</thead>
<tbody>
<tr>
<td>Does exposure to environmental lead cause measurable biological damage?</td>
<td style="text-align: center">:thumbsup:</td>
<td>Yes, lead mimics other essential minerals, causing cell death, weakening the blood brain barrier, and destroying myelin sheaths around white matter neural connections.</td>
</tr>
<tr>
<td>Is there a dose-response relationship?</td>
<td style="text-align: center">:question:</td>
<td>Maybe, but this would require a more thorough review of the literature.</td>
</tr>
<tr>
<td>Does this biological damage plausibly lead to negative individual or societal outcomes?</td>
<td style="text-align: center">:heavy_check_mark:</td>
<td>Yes, lead poisoning is a known neurotoxin, and has been demonstrably linked with reduced IQ and teenage impulse control. Lead was also a leading indicator for the rise and fall of societal violence in the 20th century, though causation can’t explicitly be determined from the available data.</td>
</tr>
<tr>
<td>Does more damage lead to even worse outcomes?</td>
<td style="text-align: center">:ok_hand:</td>
<td>Yes, a meta-analysis of IQ studies shows a well defined dose-response relationship with BLLs, with an especially steep decline at low BLLs.</td>
</tr>
<tr>
<td>Are there natural experiments happening due to varying regulations in different locations?</td>
<td style="text-align: center">:100:</td>
<td>Yes, Nevin finds that, at different times throughout the 20th century, a diverse cross section of countries experienced elevated childhood BLLs correlated with similarly time-lagged negative outcomes.</td>
</tr>
<tr>
<td>Do interventions lead to a reduction in biological damage?</td>
<td style="text-align: center">:thinking:</td>
<td>Probably. Billings and Schnepel show that an intent to treat elevated childhood BLLs both reduces BLL in subsequent tests, and also improves education and behavioral outcomes. However, limitations in the underlying data make it unclear which part of the intervention was responsible.</td>
</tr>
<tr>
<td>Does reduced damage lead to fewer negative outcomes?</td>
<td style="text-align: center">:woman_shrugging:</td>
<td>Again, probably. The same study shows that children with the steepest decline in BLL had correspondingly better outcomes, but similar caveats as above apply.</td>
</tr>
</tbody>
</table>
<h1 id="organizations">Organizations</h1>
<p>A truly comprehensive review of lead poisoning as an EA cause area would not be a complete without a GiveWell style analysis of the organizations already working in this area, but that will have to wait for another post. For now, here’s a quick rundown of some non-profits that I plan to investigate over the next giving season:</p>
<ul>
<li><a href="https://www.aap.org/">American Academy of Pediatrics</a>, which provides, among other services, resources about lead screening and treatment.</li>
<li><a href="https://clearcorpsdetroit.org/">CLEARCorps Detroit</a> is a member of the non-profit Southeastern Michigan Health Association, whose Lead Safe Homes Program abates lead hazards from homes in Detroit.</li>
<li><a href="https://www.greenandhealthyhomes.org/">Green & Healthy Homes Initiate</a>, whose mission is to “break the link between unhealthy housing and unhealthy families by creating and advocating for healthy, safe and energy efficient homes.””</li>
<li><a href="https://larcusa.org/">Lead Abatement Resource Center</a>, a Chicago-based organization whose mission is to “collect, evaluate, invent, implement, advocate and research effective solutions to lead hazards in the environment.”</li>
<li><a href="https://nchh.org/">National Center for Healthy Housing</a>’s Find It, Fix It, Fund It initiative, aimed at finding and eliminating lead hazards, and building the political will to create key public investments and policies to do so. (Note: they partially funded the Pilsen filter giveaway.)</li>
<li><a href="https://www.nsc.org/">National Safety Council</a>, which provides information about outreach and training programs that give community-based organizations the tools and skills needed for planning and executing successful lead poisoning prevention programs.</li>
</ul>Dan WahlInvestigating the case for lead poisoning as an EA cause priority.