<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" xml:lang="en"><generator uri="https://jekyllrb.com/" version="4.3.3">Jekyll</generator><link href="https://ethansager.github.io/feed.xml" rel="self" type="application/atom+xml"/><link href="https://ethansager.github.io/" rel="alternate" type="text/html" hreflang="en"/><updated>2026-04-14T17:24:40+00:00</updated><id>https://ethansager.github.io/feed.xml</id><title type="html">blank</title><subtitle>Field notes on development policy, econometrics, and research practice. </subtitle><entry><title type="html">What keeps me up writing code</title><link href="https://ethansager.github.io/blog/2025/research-interests/" rel="alternate" type="text/html" title="What keeps me up writing code"/><published>2025-12-27T12:00:00+00:00</published><updated>2025-12-27T12:00:00+00:00</updated><id>https://ethansager.github.io/blog/2025/research-interests</id><content type="html" xml:base="https://ethansager.github.io/blog/2025/research-interests/"><![CDATA[<p>I’m interested in understanding economic behaviour through rigorous measurement, applied casual analysis, and grounded testable theory that disciplines what we’re doing. My interest sits at the intersection of survey methods, and labor markets.</p> <p>A central theme in applied micro over the last decade has been the development of effective identification strategies. However, one key linchpin in this development has been the assumption of “good” data, as we know that solid identification is essential—but it doesn’t rescue you from bad data. Dillon et al. (2021) make this point explicitly: progress depends on pairing strong designs with serious attention to how the data are generated. (<a href="https://www.sciencedirect.com/science/article/abs/pii/S0305750X19304450" title="Good identification, meet good data">ScienceDirect</a>)</p> <p>That’s basically how I think about measurement. The data-generating process, both in terms of modeling the estimand we are interested in and how the data is produced, matters. I am interested in understanding the implementation details and modeling their impacts on measurement. Take, for example, mode of a survey; we know from many years of survey methodology research that respondents react differently in person versus online, partly due to certain biases. This fact is only exacerbated by selection effects of who tends to answer each of these modes in comparison to the other. However through careful study and intentional design choices, such as thinking about benchmark variables or other ways of correcting the biases we can attempt to correct for these descripencies.</p> <p>I am currently working on a project with which attempts to qauntify the measurement error and impact on treatment effects introduced by survey design choices such as how to order respondents in follow up questions. I am working with IPA, JPAL and the World Bank to develop a database of surveys to begin to conduct a meta analysis of these design choices and their impacts on treatment effects.</p> <h2 id="labor-markets-and-compensation">Labor markets and compensation</h2> <p>My interest in labor markets stems from my time in Nairobi, Kenya. Nairobi has an extremely interesting labor markets and firm behaviors. The majority of labor is informal and normally self-employment, furthermore these jobs tend to be extremely similar Labor markets are where workers, firms, and institutions bargain under frictions. I’m especially interested in compensation design—how pay structures shape worker behavior and who sorts into which jobs. Personnel economics is a useful backbone here because it treats pay schemes as an equilibrium object: incentives matter, but selection and sorting matter too. Lazear’s classic evidence from a shift to piece rates shows both channels in a way that’s empirically clean and conceptually tight. (<a href="https://www.aeaweb.org/articles?id=10.1257%2Faer.90.5.1346" title="Performance Pay and Productivity">American Economic Association</a>)</p> <p>Substantively, I’m interested in:</p> <ul> <li>how firms split compensation between cash and non-cash margins,</li> <li>how wage-setting interacts with frictions (search, bargaining, mobility),</li> <li>and how we measure job quality in a way that goes beyond “wage = welfare.”</li> </ul> <h2 id="bringing-it-together">Bringing it together</h2> <p>The way I see it: <strong>measurement is upstream of everything</strong>. Better measurement makes labor market inference sharper, and serious labor market work forces you to model firms rather than treating them as a black box. My goal is to do work that’s empirically credible <em>because</em> it’s grounded in how the data were produced, and theoretically grounded <em>because</em> the theory clarifies what should move, for whom, and why.</p>]]></content><author><name></name></author><category term="research"/><category term="economics"/><summary type="html"><![CDATA[An overview of my research areas in measurement, and labor markets]]></summary></entry><entry><title type="html">A blog about blogs I like</title><link href="https://ethansager.github.io/blog/2025/blogs-I-like/" rel="alternate" type="text/html" title="A blog about blogs I like"/><published>2025-12-27T00:00:00+00:00</published><updated>2025-12-27T00:00:00+00:00</updated><id>https://ethansager.github.io/blog/2025/blogs-I-like</id><content type="html" xml:base="https://ethansager.github.io/blog/2025/blogs-I-like/"><![CDATA[<p>I read a couple blogs. Here are some of my favorites organized by topic.</p> <h2 id="data-blogs">Data Blogs</h2> <ul> <li><a href="https://blog.djnavarro.net/">Notes from a data witch</a>: Navarro writes about statistics, R, and data science with interesting tech tid bits.</li> <li><a href="https://datacolada.org/">Data Colada</a>: Mostly follow to keep up with interesting deep dives into replication drama.</li> <li><a href="https://www.argmin.net/">arg min</a>: Ben Recht has a great set of posts from his class on machine learning it also goes along with his book Patterns, Predictions, and Actions.</li> <li><a href="https://statmodeling.stat.columbia.edu/">statmodeling</a>: Andrew Gelman is a prolific blogger in statistics and data science. I mostly follow to keep up with his thoughts on Bayesian statistics.</li> </ul> <h2 id="development-economics">Development Economics</h2> <ul> <li><a href="https://developmentekko.substack.com/">Development Ekko</a>: Julie Zollmann is a wonderful person and an excellent writer who mostly focuses on topics you don’t see talked about much such as the frequency of mobile money usage.</li> <li><a href="https://www.global-developments.org/">Global Developments</a>: Oliver Kim writes mostly about historical development economics and does a great job of connecting disparate topics.</li> <li><a href="https://voxdev.org/blogs">VoxDev</a>: Alot of the blogs are written by Oliver Hanney who I really enjoy reading but they also have guest bloggers.</li> <li><a href="https://blogs.worldbank.org/en/impactevaluations">Development Impact</a>: One of the few blogs that covers really specific design and measurement issues in depth. I get excited anytime David McKenzie writes a post.</li> </ul> <h2 id="politics-of-development">Politics of Development</h2> <ul> <li><a href="https://www.africanistperspective.com/">An Africanist Perspective</a>: Ken Opalo who also happens to be a professor at Georgetown writes about both macro and micro issues with a interesting lense pushing back on the simple narratives that are often presented.</li> <li><a href="https://katieauth.substack.com/">Aid Interrupted</a>: Katie Auth writes about the poltics of foreign aid with a focus on the US.</li> <li><a href="https://toddmoss.substack.com/">Eat More Electrons</a>: Todd Moss who also has a great book on the politics of development in Africa writes about the how eletricity access and energy policy is inherently political.</li> </ul> <h2 id="public-intelectuals-cosmopolitan-polymaths-or-just-some-people-who-write-on-a-variety-of-topics">“Public Intelectuals” “Cosmopolitan polymaths” or just some people who write on a variety of topics</h2> <ul> <li><a href="https://www.noahpinion.blog/">Noahpinion</a>: I have a love hate relationship with this blog at times me and Noah Smith are really on the same wavelength both topically and argumentively other times it feels like he just had to write a post which of course I get given his profession.</li> <li><a href="https://worksinprogress.co/">Works in Progress</a>: Though technically a magazine this is a great collection of long form essays on a variety of topics I loved Brian Albrecht’s piece on using the Olley-Pakes decomposition to better understand monopolies.</li> </ul>]]></content><author><name></name></author><category term="misc"/><category term="data-science"/><category term="economics"/><category term="politics"/><summary type="html"><![CDATA[mostly data science but some economics and politics as well]]></summary></entry></feed>