Experimenting with Development Economics

A Review of Debunking the Stereotype of the Lazy Welfare Recipient: Evidence from Cash Transfer Programs World Wide

As I scrolled through Twitter a couple of months ago, I found a New York Times review of a recent economics paper that drew my interest. It caught my eye because the lead author is Abhijit Banerjee. Banerjee is all the co-author (with Esther Duflo) of what is probably my favorite economics book written for a general audience, Poor Economics: A Radical Rethinking of the Way to Fight Global Poverty.

I was not necessarily interested in the subject matter of this particular paper (though it turned out to be a fun read), as much I wanted excuse to write about Banerjee's work. But it's a been a while since read Poor Economics (and the other books I reference), so this seemed like a reasonable entry point. 

Lessons from a Burning Bronx

What the New York City Fire Epidemic Can Teach Today's Analysts: A Review of the "The Fires"

In 1977, The New York Yankees faced off against the Los Angeles Dodgers in the World Series. Before one of the games, a fire broke out near the stadium. Audiences seem to remember the announcer,  Howard Cosell saying "There it is, ladies and gentleman, the Bronx is burning" as the Yankees lost and camera panned upwards to show the lingering smoke. This phrase captured the essence of the game, and more importantly epidemic of fires in the city. It even became the title of a ESPN television show about the World Series. Never-mind that the actual footage of the game doesn't include this phrase. 

Though the quote may be apocryphal, there truly was an epidemic of fires throughout New York City in the 1970s. I was turned onto a fascinating history of these fires, listening to What's The Point, a podcast from fivethirtyeight.  This podcast contained a conversation with Joe Flood, author of The Fires How a Computer Formula, Big Ideas, and the Best of Intentions Burn Down New York City-- and Determined the Future of Cities.

This history, in part, involves data, complex analyses, and the RAND Corporation. This of course, was fascinating to me, as I went graduate school at the Pardee RAND Graduate School (for all practical purposes, part of RAND), so I read Flood's book. Flood's story of the fires reminded me of some prescient lessons, that in some form are relevant to many analysts and data scientists to.

Stupid Data Tricks

A Markov-Chain Text Generator for Evanometrica

You will all have to excuse me, as it has been a little bit longer than I was expecting since my last post. First, I was traveling. Second, I found that it is a little more time intensive to generate high quality blog-posts than I initially assumed.

Being a data scientist and all, I figure I can just get the computer to do it for me! I decided to write an algorithm that can come up with headlines of blog-posts for me. It's an extremely simple Markov-Chain Model text generator, that leads to some entertaining results. This was fun, but accomplishes nothing of value, so it was a Stupid Data Trick.

Data Science, Econometrics and Climate Adaptation. OH MY!

A review of  Hsiang and Narita's  Adaptation to Cyclone Risk: Evidence From the Global Cross Section from 2012

This month, a world leaders and scientists are  engaging in a new round of climate talks in Paris. I personally believe climate change is one of the most important policy issue of the times, and hope you all have been following these talks in the news. When most people think about climate change, especially within the U.S. political context, they think about mitigation. Mitigation refers to efforts to curb carbon emissions and prevent further climate change. This is contentious for two reasons. First, some people like to claim that carbon emissions from humans are not the cause of climate change. Second, given our current technology it is legitimately expensive curb emissions, and in one way or another this will impose real costs on everybody in society. Of course, I agree with scientific consensus that human emissions are the cause of climate change. Most importantly, I have a great fear that the costs of not preventing climate change will be even more expensive in the long run than the costs of curbing them.

However, there is another part of the climate equation—one that is less controversial and sexy: adapting to climate change. Even if we stopped emitting all carbon today, warming would continue for decades.  Instead, we need to figure out ways to live with it. We can change our lives, our cities, and our resource planning to adjust for the uncertain future that comes with climate change. This is a topic that I could go on at length about. Wait, I did for nearly 200 pages in my PhD dissertation. I did some interesting (well, I guess the word "interesting" is debatable) research proposing planning approaches to adapt.

There is a whole other line of empirical research, examining the extent to which people already adapt to their climate. This research is suggestive of how some countries, states or cities may adapt in the future. To keep myself sharp on this topic, in this post I review paper estimating the extent to which countries have adapted to the threats of tropical cyclones (commonly known as hurricanes or typhoons). This is important for climate change, because all the evidence suggests that climate change will increase the frequency and intensity of tropical cyclones. The paper is Hsiang and Narita's  Adaptation to Cyclone Risk: Evidence From the Global Cross Section from 2012. And its really freaking cool, even if you don't have a PhD studying climate change!

21-0 Status Check

A Quick Update on the Warriors 

I am getting far more milage out of my Warriors model than I ever expected, as they have made it to 21-0. If you are interested in reading how I do my analysis, check out my post on it here. But, almost two weeks and five wins laters, I just wanted to give you an update on how the Warriors' projections are doing. 

Paradigm Lost?

A somewhat long and rambling consideration of Kuhn's The Structure of Scientific Revolution

In college, I double majored Political Science and Management Science. I now work in a field called Data Science. Even though the last time I sat in a physics class was my junior year of high school, I am a scientist. One of my college buddies, an engineer, liked to poke fun at me, "if they have to put science in the name, I am pretty sure it's not science." Despite my degrees, I agreed with him. Seven years and one PhD later, my thoughts on science have changed. I no longer define science so much by the subject matter as by the process. Science is not limited to applications of physics, chemistry, or biology; human behavior, interactions between people, and society at large can also be topics of scientific inquiry.


Science isn't so much defined by subject matter as by a process. This process is, of course, the scientific method: generating hypothesis and testing the hypothesis by observation or experimentation. Unsurprisingly, there is a science of science; which is excellently described (and probably really began) in Thomas Kuhn's The Structure of Scientific Revolution. In this book, Kuhn proposes a theory of how large scientific breakthroughs,"revolutions", are arrived at. To do so, he also establishes a framework for how science functions outside periods of revolution. I found this framework both immensely useful for understanding the ebbs and flows of science, but also questioned whether "revolution" is as unique as Kuhn seems to imply.

16-0 and I feel fine

When I last posted after a Golden State Warrior's win, I was a little dismayed. Their win shed light on some fundamental flaws in my Bayesian updating model. Now, I have no such trepidation. Warriors moved to 16-0 (an unprecedented NBA record) and I spent my weekend coding and doing statistics (a very precedented Evan activity)

Podcast Power Ranks

I listen to a ton of Podcasts, something like three hours a day. It's a lot. I have a 45 minute commute each way. I work out for another 1 to 1.5 hours most days. Add in doing chores around the house (cooking, dishes, etc), and the moments before I fall asleep. Why not use every moment of that to suck in more information?

Here is a completely subjective ranking of my top 35 podcasts, based on no explicit criteria at all:

Robustness Check

The Warriors are 13-0. Normally, I would ecstatic but it revealed a big problem in my model.  My model now says the Warriors have a 0 percent chance of repeating their performance from last year, no matter your prior beliefs. Some people would say that Steph Curry is the ultimate heat check, but in this case he is the robustness check (I know, that was extremely lame... I'm just gonna own it).

Give Them Credit?

So there is thing about people, we're not entirely rational. Instead of always objectively weighing the costs and benefits of every decision, sometimes we are affected by our mood or compare ourselves to others. These irrationalities are well-documented by behavioral economist like Daniel Kahneman and Dan Ariely.

One of the ways we are irrational is our preference for instant gratification, something the behavioral economists refer to as hyperbolic discounting. This leads to all sorts of common human behaviors, such as procrastination. Doing some work now feels really high cost, but it seems like it will be less of a chore in the future. Of course, when that future time rolls around, it again feels high cost. So those of us that are irrational fall into a cycle of procrastination. Its probably fair to say we all procrastinate to some degree, so we are all irrational.  

Come Out and Play

The Learning Problem

The Golden State Warriors have started the NBA season 11-0. If the team keeps up this pace, they will win all 82 games, sweep every round of playoffs, and never ever lose again. Trust me, I'm a data scientist.

Something sounds fishy here, right? Obviously, I don't expect the team to win every single game just because they haven't lost one yet. Why? For one thing no team has ever gone 82-0, so I consider that pretty unlikely. For another thing, the Warriors were a great team (some would say historically great) last year and they "only" won 81.7 percent of their games. So can I really expect them to be that much better?

On the other hand, seeing how they have performed so far, I must have learned something about how amazingly awesome they are.  The question is, how do I balance the information obtained from the first few games, with whatever else I know (or at least believe) about the team.

Books I've Read In 2015

This is a somewhat boring post; simply listing the books I have read in 2015. They are listed in approximate order of totally subjective importance, interest, and general awesomeness. I will keep this post updated as I add more books. Over the next few months, I will probably write some posts discussing the more interesting of these books.

Thoughts on "Between the World and Me"

I recently finished the most powerful book I have read in years: "Between the World and Me" by Ta-Nehesi Coates. Frankly, it is not the type of book I often read; it is short and contains almost no numbers or statistics. Instead, the book is letter from the author to his son on the experience of being a black man in America today. The specifics of his story are powerful, the philosophizing is important, and the writing is quite beautiful (something which I rarely dwell on).

What is evanometrica?

Now that I have completed my PhD, I find myself with some excess free time. In this free time, I am rediscovering old hobbies, such as reading for fun, and identifying new hobbies, such as playing with computers. Because I find these activities so interesting, I figure others might also.

In my final year of graduate school, I spent hour after hour writing and rewriting. I hope I picked up a skill; one that I don't think I was very good at previously. In my current job, I don't have to write very much. In an attempt to make sure my skills don't degrade, I figured I would start a blog. 

So what exactly is the blog about? Well, its about evanometrica: the data, statistics, and economics of all things evan. I am still not exactly sure what this will mean, but I do have a couple of ideas.  I imagine posts will be broken down into roughly four frequently over-lapping categories: data, economics, books, and sports.