Noble work: Anand Giridharadas on the EKS

There was a recent discussion on the IDinsight #philosophy Slack channel about a recent Ezra Klein Show (EKS on this blog from now on, since I talk about it all the time) podcast with Anand Giridharadas. My contribution built off someone else’s notes that Giridharadas is spot on about how companies (also IDinsight in some ways) sell working for them as an extension of the camaraderie and culture of a college campus, how he doesn’t offer concrete solutions and that’s very annoying, and some reflections on transitioning from private sector consulting to IDinsight’s social sector, non-profit consulting model. I related more to the moral arguments in the podcast, and this is what I shared:

I connected most with his argument about how the overall negative impact of many big for-profit companies on worldwide well-being vastly outweighs any individual good you can do with the money you earn. One of EA’s recommended pathways to change is making a ton of money and giving it to effective charities, but if you do that by working for an exploitative company, then you’re really contributing to the maintenance of inequality and of the status quo racist, sexist, oppressive system.

My dad was always talking about having a “noble” profession when I was growing up (he’s a teacher and my mom’s a geriatric physical therapist) and even though “noble” is a strange way to put it, I think it is really important to (as much as possible) only be party to organizations and companies that are doing good or at least not doing active harm.

That being said, there are more reasons for going into the private sector and aiming to make money than are really dealt with in the podcast. For example, a few people we’ve talked to in South Africa have mentioned that many highly skilled South Africans are responsible for the education costs for all siblings/cousins and that is a strong motivator to take a higher paying salary.

It becomes very related to the debate about how much development or social sector workers should get paid, relative to competitive private sector jobs. I think IDinsight does a pretty good job of being in the middle for US associates anyways – paying enough that you can even save some, which is more than a lot of non-profits provide, but not necessarily trying to compete with private sector jobs because our model relies a lot on hiring people who are in it to serve, not for the money. Something for us to continue thinking about is how this might exclude candidates who have other financial responsibilities and how we should respond to this issue in how we hire and set salaries.

It’s so frustrating when people identify a problem without offering solutions. The closest he comes to offering solutions is to have organizations stop lobbying for massive tax breaks or in other ways deprioritize the bottom line of profitability. Sounded to me like his vision involves a lot more socialist ideas: the full solutions to these issues would involve massive-scale reorganizing of the existing economic system… although maybe we are heading in that direction with more co-op style companies and triple bottom line for-profit social enterprises? (Don’t know a ton about this co-op stuff – mostly from another Ezra Klein show episode probably, but it sounds cool!) …Maybe his next book will try to map out solutions, though?

Look out for: Market impacts of cash transfers

Forthcoming – “General Equilibrium Effects of Cash Transfers,” from Paul NiehausJohannes HaushoferTed Miguel and Michael Walker, answering the question: What are the market impacts of an inflow of ~15% of local GDP?

I have to say I barely understand what’s going on in a “market” … my economic background is very individual- and household-focused.

But understanding the effect of an intervention on a community as a whole, not just on those treated, seems really important. Partially, this is why we look at and consider spillover effects – the effect of an intervention on the neighbors of the treated, who didn’t receive the program themselves.

General equilibrium or market effects investigate a level up from spillover effects and treatment effects – they look at the cumulative impact of the program to the way the economy operates.

I couldn’t explain how one studies a specific market, or what counts as being part of the market, in any clear terms, but I’m still excited for this upcoming paper on the market-level effects of cash transfers, a point that has been debated recently, after evidence of potential negative spillover effects came out.

 

I’m pretty sure I just solved life

Disclaimer: I was a little drunk on power (calculations) when I wrote this, but it’s me figuring out that econometrics is something I might want to specialize in!

I think I just figured out what I want to do with the rest of my career.

I want to contribute to how people actually practice data analysis in the development sector from the technical side.

I want to write about study design and the technical issues that go into running a really good evaluation, and I want to produce open source resources to help people understand and implement the best technical practices.

This is always something that makes me really excited. I don’t think I have a natural/intuitive understanding of some of the technical work, but I really enjoy figuring it out.

And I love writing about/explaining technical topics when I feel like I really “get” a concept.

This is the part of my current job that I’m most in love with. Right now, for example, I’m working on a technical resource to help IDinsight do power calculations better. And I can’t wait to go to work tomorrow and get back into it.

I’ve also been into meta-analysis papers that bring multiple studies together. In general, the meta-practices, including ethical considerations, of development economics are what I want to spend my time working on.

I’ve had this thought before, but I haven’t really had a concept of making that my actual career until now. But I guess I’ve gotten enough context now that it seems plausible.

I definitely geek out the most about these technical questions, and I really admire people who are putting out resources so that other people can geek out and actually run better studies.

I can explore the topics I’m interested in, talk to people who are doing cool work, create practical tools, and link these things that excite me intellectually to having a positive impact in people’s lives.

My mind is already racing with cool things to do in this field. Ultimately, a website that is essentially an encyclopedia of development economics best practices would be so cool. A way to link all open source tools and datasets and papers, etc.

But top of my list for now is doing a good job with and enjoy this power calculations project at work. If it’s as much fun as it was today, I will be in job heaven.

Continue reading I’m pretty sure I just solved life

Recommendations of the Week: June 18-24

Blog

Goddess-Economist Seema Jayachandran wrote about economists’ gendered view of their own discipline back in March. Dr. Jayachandran and PhD student co-author Jamie Daubenspeck investigate:

  1. Percent of woman authors on different development topics: Drawing on all empirical development papers from 2007-2017, they find, out of all papers, “51% were written by all men, and 15% by all women. The average female share of authors was 28% (weighting each paper equally).” Gender, health, trade, migration, education, poverty and conflict are the development topics with a greater than average number of woman authors.
  2. Economists’ perspectives on under-researched topics: They show that there is a negative correlation between a topic’s % of woman authors and perceptions the topic is under-researched, a finding they call “a bit depressing.” Same. (They also write that “whether a topic is under-researched are not significantly correlated with the actual number of articles on the topic published in the JDE over our sample period.” So what do these economists even know?)

I love their thoughtful outline of the methodology they used for this little investigation. Describing the world with data is awesome.

Awesome Humans

I ended up hearing about/reading about several amazing humans this week:

Dr. Nneka Jones Tapia – the clinical psychologist running Cook County Jail – had amazing things to say on the Ezra Klein Show last year in July. She is powerful and thoughtful and doing amazing things to improve prisons in the US.

New Zealand PM Jacinda Ardern gave birth on the 21st. She’s only the second world leader to give birth in office, after Pakistan’s Benazir Bhutto. The best part is that she is 100% unapologetic about being a mother in office, even while she acknowledges the challenges she will personally face in balancing a new baby and work.

These two leaders are just out there in the world leading noble, thoughtful, innovative lives. In love.

And then there’s MJ Hegar, who’s running for Congress against a tea partier in Texas. Her amazingly directed ad shows how enduring her dedication to service has been throughout her life:

Life Skill

My best friend Riley and I made a pact to meditate daily for ten days, starting on Monday. I have done it each day this week and my week has felt fuller and more focused than ever. Not willing to attribute full causality to the meditation, but it definitely has been a tool to start my day well and a reminder throughout the day that I can and want to stay focused and in the moment.

Podcast

The Ezra Klein Show interviews are always on point, and “The Green Pill” episode featuring Dr. Melanie Joy was no exception. The June 11 show discussed “carnism” – the unspoken ideology that tells us eating animals, wearing animals, and otherwise instrumentalizing them is good.

I’ve been mulling it over for a while now, but the episode’s frank conversation about why veganism is so hard to talk about pleasantly – and why it’s so hard for people to shift from a carnal mindset – motivated me to head back down the vegetarian path.

I was vegetarian for a year or so in college, but now I’m aiming for veganism, or something close. I’m not eating meat and am not actively purchasing or eating eggs or milk. At this point, I’ll eat eggs or milk or other animal products that are already baked into something – a slice of cake, for example. Eventually, I want to phase out pretty much all animal products. But I’m giving myself some space to adjust and dial back the carnism bit by bit. The incremental approach should let me stick to it better.

Cheese will probably be my “barrier food” – apparently this is so common, there’s a webpage that specifically teaches how to overcome the cheese block. (hehe)

They recommend slowly replacing cheese with guac or hummus, and taking a large break from any cheese before trying vegan cheese. (Which won’t be a problem since I doubt there’s any vegan cheese in Kenya to begin with!)

Cover Image

Fruit

It is not mango season in Kenya, but I had the best mango this week. Maybe because I cut it myself for the first time, making an absolute mess. Or maybe because it was the key ingredient to the first lettuce-containing salad I’ve ever made myself at home. But there’s a lot to be said for a fruit that encourages you to embrace your messy nature.

Why you should convert categorical variables into multiple binary variables

Take the example of a variable reporting if someone is judged to be very poor, poor, moderately rich, or rich. This could be the outcome of a participatory wealth ranking (PWR) exercise like that used by Village Enterprise.

In a PWR exercise, local community leaders can identify households that are most vulnerable. These rankings can then be used to target a development program (like VE’s graduation-out-of-poverty program that combines cash transfers with business training) to the community members that are most in need.

Let’s say that you want to include the PWR results in a regression analysis as a covariate. You have a dataset of all the relevant variables for each household, including a variable that records whether the household was ranked in the PWR exercise as very poor, poor, moderately rich, or rich.

You need to convert this string variable (text) into a numeric value. You could assign each option a value from 1 to 4, with 1 being “very poor” and 4 meaning “rich” … but you shouldn’t use this directly in your regression.

If you have a variable that moves from 1 to 2 to 3 to 4, you’re implying that there is a linear pattern between each of those values. You’re saying that the effect on your outcome variable of going from being very poor (1) to poor (2) is the same as the effect of going from poor (2) to moderately rich (3). But you don’t know what the real relationship is between the different PWR levels, since the data isn’t that granular. You can’t make the linear assumption.

So instead, you should use four different binary variables in your regression: Ranked “very poor” or not? “Poor” or not? “Moderately rich” or not? “Rich” or not?

This Stata support page does a great job of summarizing how to apply this in your regression code or create binary variables from categorical using easy shortcuts. I like:

reg y x i.pwr

But how do you interpret the results?

When you create dummies (binary variables) out of a categorical variable, you use one of the group dummies as the reference group and don’t actually include it in the regression.

By default, the reference group is usually the smallest/lowest group. In this case, that means “very poor.” So in the regression, you’ll have three dummies, not four. Being “very poor” is the base condition against which to compare the other rankings.

Let’s say there is a statistically significant, positive coefficient on the “moderately rich” dummy in your regression results. That means that, compared to the base condition of being very poor, being moderately rich has a positive effect on your outcome variable.

Ӧzler: Decrease power to detect only a meaningful effect

Photo by Val Vesa on Unsplash

Reading about power, I found an old World Bank Impact Evaluations blog post by Berk Ӧzler on the perils of basing your power calcs in standard deviations without relating those SDs back to the real life context.

Ӧzler summarizes his main points quite succinctly himself:

“Takeaways:

  • Think about the meaningful effect size in your context and given program costs and aims.
  • Power your study for large effects, which are less likely to disappear in the longer run.
  • Try to use all the tricks in the book to improve power and squeeze more out of every dollar you’re spending.”

He gives a nice, clear example to demonstrate: a 0.3 SD detectable effect size sounds impressive, but for some datasets, this would really only mean a 5% improvement which might not be meaningful in context:

“If, in the absence of the program, you would have made $1,000 per month, now you’re making $1,050. Is that a large increase? I guess, we could debate this, but I don’t think so: many safety net cash transfer programs in developing countries are much more generous than that. So, we could have just given that money away in a palliative program – but I’d want much more from my productive inclusion program with all its bells and whistles.”

Usually (in an academic setting), your goal is to have the power to detect a really small effect size so you can get a significant result. But Ӧzler makes the opposite point: that it can be advantageous to only power yourself to detect what is a meaningful effect size, decreasing both power and cost.

He also advises, like the article I posted about yesterday, that piloting could help improve power calculations via better ICC estimates: “Furthermore, try to get a good estimate of the ICC – perhaps during the pilot phase by using a few clusters rather than just one: it may cost a little more at that time, but could save a lot more during the regular survey phase.”

My only issue with Ӧzler’s post is his chart, which shows the tradeoffs between effect size and the number of clusters. His horizontal axis is labeled “Total number of clusters” – per arm or in total, Bert?!? It’s per arm, not total across all arms. There should be more standardized and intuitive language for describing sample size in power calcs.

Gendered language -> gendered economic outcomes

A new paper by Jakiela and Ozier sounds like an insane amount of data work to classify 4,336 languages by whether they gender nouns. For example, in French, a chair is feminine – la chaise.

They find, across countries:

  • Gendered language = greater gaps in labor force participation between men and women (11.89 percentage point decline in female labor force participation)
  • Gendered language = “significantly more regressive gender norms … on the magnitude of one standard deviation”

Within-country findings from Kenya, Niger, Nigeria, and Uganda – countries with sufficient and distinct in-country variation in language type – further show statistically significant lower educational attainment for women who speak a gendered language.

(Disclaimer: The results aren’t causal, as there are too many unobserved variables that could be at play here.)

As the authors say: “individuals should reflect upon the social consequences of their linguistic choices, as the nature of the language we speak shapes the ways we think, and the ways our children will think in the future.”