Conference Recap: PacDev 2016

I had the pleasure of driving down to rainy Stanford for PacDev last weekend. Aside from the obviously-most-important benefit of having fondue for dinner at my parents' house, I got to present my ongoing work with Louis. Our paper, on the effects of electrification on economic development in India, is finally almost ready to see the light of day (for real this time, we swear!) - there will of course be an announcement on this blog when that happens. But enough about me. I also got to see a bunch of interesting new papers in a diverse range of subsets of development economics, and I'm pretty excited about them. A few highlights:

  • I went to an (early) morning session on taxes, which I know almost nothing about, and really enjoyed all four papers I saw: Anne Brockmeyer's on third-party information and withholding decisions in Costa Rica; Spencer Smith's on some cool experimental evidence on information (also in Costa Rica); Jose Tessada's on regulations about firm gender ratios and capital substitution; and the always-excellent (follow her on Twitter!) Dina Pomeranz's on multinational corporations and tax havens.
  • Yong Suk Lee (previously a professor at Williams!) has an interesting new paper looking at the effect of sanctions in North Korea on economic activity. He shows that the sanctions and subsequent shifts in international trading partners led to changes in the locational composition of economic activity, as proxied by nighttime lights. While I'm always wary of the use of nightlights to proxy for sub-national GDP, this paper deals with these issues nicely, making within-country comparisons between regions and talking about relative concentration and identification. Plus, we have so little to go on with North Korea. Cool stuff.
  • I also went to two political economy sessions - like economic history, political economy often has some of my favorite papers of conferences like this. I particularly enjoyed listening to my old boss, Saumitra Jha, talk about an amazing experiment in which giving Israelis stocks led them to be more willing to vote for peace-promoting government, and hearing Kweku Opoku-Agyemang (a Berkeley post-doc), talk about the effects of raising police salaries on bribery.

All in all, a great conference. Hopefully I'll get to go back next year!

If you read all the way down here, here's a reward. Amazing. h/t Josh

WWP: Rain, rain, go away, you're hurting my human capital development

Greetings from String Matching Land! Since I seem to be trapped in an endless loop of making small edits to code and then waiting 25' for it to run (break), I'm going to break out for a little bit and tell you about a cool new paper that I just finished reading. Also, it's Wednesday, so my WWP acronym remains intact. Nice.  

Manisha Shah (a Berkeley ARE graduate!) at UCLA and Bryce Millett Steinberg, a Harvard PhD currently doing a postdoc at Brown who is on the job market this year (hire her! this profession needs more women!), have a new paper that I really like. I thought of this same idea (with slightly different data) recently, and then realized that this is forthcoming in the JPE (damn)- and it's excellently done. The writing is clear, the set-up is interesting, the data are cool, the empirics are credible, and the results are intuitive. Did I mention it's forthcoming in the JPE? 

In this paper, Shah and Steinberg tackle a prominent strand of development economics: what do economic shocks do to children at various stages of growth? There's a long literature on this, including the canonical Maccini and Yang paper (2009 AER), who find that good rainfall shocks in early life dramatically improve outcomes for women as adults (in Indonesia). This paper does a great job of documenting a treatment effect (if you haven't read it yet, metaphorically put down this blog and go read that instead), but less to say about the mechanisms behind it.

Steinberg and Shah take seriously the idea that rainfall shocks might affect human capital through multiple channels: good rain shocks could mean more income, and therefore consumption and human capital, or good rain shocks might mean a higher opportunity cost of schooling, leading to less education and human capital development. They put together a very simple but elegant model of human capital decisions, and test its implications using a large dataset including some simple math and verbal tests from India.  They show that good rain shocks are beneficial for human capital (as proxied by test scores) early in life, but lead to a decrease in human capital later in life. They demonstrate that children are in fact substituting labor for schooling in good harvesting years, and show that rainfall experienced in childhood matters for total years of schooling as well, which could help explain the Maccini and Yang result, though they don't find differential effects by gender. 

In the authors' own words (from the abstract):

Higher wages are generally thought to increase human capital production, particularly in the developing world. We introduce a simple model of human capital production in which investments and time allocation differ by age. Using data on test scores and schooling from rural India, we show that higher wages increase human capital investment in early life (in utero to age 2) but decrease human capital from ages 5-16. Positive rainfall shocks increase wages by 2% and decrease math test scores by 2-5% of a standard deviation, school attendance by 2 percentage points, and the probability that a child is enrolled in school by 1 percentage point. These results are long-lasting; adults complete 0.2 fewer total years of schooling for each year of exposure to a positive rainfall shock from ages 11-13. We show that children are switching out of school enrollment into productive work when rainfall is higher. These results suggest that the opportunity cost of schooling, even for fairly young children, is an important factor in determining overall human capital investment.
Obligatory stock photo of Indian school kids during the rainy season. Obviously not my own photo.

Obligatory stock photo of Indian school kids during the rainy season. Obviously not my own photo.

A few nitpicky points: I could've missed this, but the data are a repeated cross-section rather than a panel of students, so I wanted a little more discussion of whether selection into the dataset was driving the empirics. Also, when they start splitting things by age group, I'm surprised that they still have enough variation in test performance among 11-16-year-olds to estimate effects. I would've expected these students to max out the test metrics, given that the exam being administered is incredibly basic numeracy and literacy skills. But maybe not. Finally, since I'm teaching my 1st-year econometrics students about figures soon, these graphs convey the message but aren't the sexiest. Personal gripe. All in all, though, this is a really nice paper - I urge you to go read it. 

A final caveat: this is of course context-specific. I don't at all mean to suggest (and nor do the authors), for instance, that these results should have Californians glad that we're done with the rain and back to sunny weather. As much as I enjoy sunrise runs (n = 1) and sitting outside reading papers, I'd be happy with a little more of what El Nino's got to offer the West Coast.

WWP: Old fight, new tricks?

One of the most interesting papers I saw at the ASSA meetings in January was Ariel Ortiz-Bobea's new work on the climate and agriculture question. For anyone not in the know, there is a long (read: loooooooong) literature trying to estimate the effects climate change will have on agriculture. Most of this debate has focused on the US, largely for data reasons (and partly because US maize is way sexier than Kenyan maize...amirite?).  

An overly brief summary of this literature is the following:

  • In the Beginning, agronomists created the Crop Model. These models were created using test plots, and used to predict the effects of climate on agriculture.
  • Then, some economists came along, and made the point that the agronomists were selling the farmers short. Crop models ignore the potential for farmer adaptation. And thus the Ricardian model was born: these economists regress land values on average temperatures, plus a bunch of controls, and find mild-to-positive effects of climate change. 
  • But wait! Enter Team ARE. A second set of economists argued that the Ricardian approach, like most cross-sectional regressions, suffered from omitted variable bias. In particular, they note that the presence of irrigation dramatically changes agriculture, and suggested estimating different models for irrigated and non-irrigated regions (if you're keeping score at home, you can also implement this suggestion via an interacted model). When they account for irrigation, climate change looks pretty bad again.
  • A few years later, some other economists arrived on the scene. If you're worried about irrigation, they argued, you should be worried about a whole host of other omitted variables in the cross-section. But do we have the idea for you? These guys used a panel fixed effects model to remove time-invariant omitted variables - also sparking a debate about "weather vs. climate" (using short-run fluctuations rather than long-run variation to estimate the model in question) - and find again that climate change probably isn't so bad.
  • Unnnnnfortunately, our panel-data-wielding heroes had some data problems (brought to light by Team ARE). If you correct them, climate change harms US agriculture to the tune of tens of billions of dollars. Oops.
  • But the weather-vs-climate thing is still unsatisfying! So Team ARE: The Next Generation used a long-difference estimator to show that actually, farmers don't seem to be doing a better job responding to climate change over time - it'll still be bad.

Here's where Ariel's new paper comes along. He notes that (for various reasons glossed over above) we might actually want to run a Ricardian-style model: in essence, weather vs. climate hasn't been fully resolved. At the same time, though, we should be worried about omitted variable bias. But in particular, we should be worried about spatially-dependent omitted variable bias. The argument is pretty simple. Most things that might be left out of an agriculture-climate regression that would bias that regression vary smoothly over space. Conveniently for the econometrician, there are some newer estimators that we can use to understand the magnitude and direction of the bias that might result from these types of omitted variables. Ariel uses these tricks, and finds that (lo and behold) climate change might not be so bad for agriculture in the US after all.

Effects of climate change estimated using OLS: this is the original economist version.

Effects of climate change estimated using OLS: this is the original economist version.

New-fangled effects of climate change using the Spatial Durbin Model. Note the lack of hugely negative effects, especially towards the right of the figure.

New-fangled effects of climate change using the Spatial Durbin Model. Note the lack of hugely negative effects, especially towards the right of the figure.

This paper is full of technical details, makes some fairly strong structural assumptions about exactly how omitted variables vary over space, and ends up with fairly wide confidence intervals, but all in all, it makes a useful contribution to an important debate, and is worth a read. I'll be interested to see where it ends up, and how seriously the literature takes the re-posited suggestion that climate change really isn't that bad for US ag. If nothing else, this paper highlights just how important it is for us to figure out how to measure adaptation!

Bonus: If you've read this far down, you deserve something fun. Go check out my new favorite internet game. h/t Susanna & Paula.


Edited to fix links. Thanks to my usual blog-police for pointing this out.

Standard errors in Stata: a (somewhat) cautionary tale

Last week, a colleague and I were having a conversation about standard errors. He had a new discovery for me - "Did you know that clustered standard errors and robust standard errors are the same thing with panel data?"

I argued that this couldn't be right - but he said that he'd run -xtreg- in Stata with robust standard errors and with clustered standard errors and gotten the same result - and then sent me the relevant citations in the Stata help documentation. I'm highly skeptical - especially when it comes to standard errors - so I decided to dig into this a little further. 

Turns out Andrew was wrong after all - but through very little fault of his own. Stata pulled the wool over his eyes a little bit here. It turns out that in panel data settings, "robust" - AKA heteroskedasticity-robust - standard errors aren't consistent. Oops. This important insight comes from James Stock and Mark Watson's 2008 Econometrica paper. So using -xtreg, fe robust- is bad news. In light of this result, StataCorp made an executive decision: when you specify -xtreg, fe robust-, Stata actually calculates standard errors as though you had written -xtreg, vce(cluster panelvar)- !


Standard errors: giving sandwiches a bad name since 1967.

Standard errors: giving sandwiches a bad name since 1967.


On the one hand, it's probably a good idea not to allow users to compute robust standard errors in panel settings anymore. On the other hand, computing something other than what users think is being computed, without an explicit warning that this is happening, is less good. 

To be fair, Stata does tell you that "(Std. Err. adjusted for N clusters in panelvar)", but this is easy to miss - there's no "Warning - Clustered standard errors computed in place of robust standard errors" label, or anything like that. The help documentation mentions (on p. 25) that specifying -vce(robust)- is equivalent to specifying -vce(cluster panelvar)-, but what's actually going on is pretty hard to discern, I think. Especially because there's a semantics issue here: a cluster-robust standard error and a heteroskedasticity-robust standard error are two different things. In the econometrics classes I've taken, "robust" is used to refer to heteroskedasticity- (but not cluster-) robust errors. In fact, StataCorp refers to errors this way in a very thorough and useful FAQ answer posted online - and clearly states that the Huber and White papers don't deal with clustering in another.

All that to say: when you use canned routines, it's very important to know what exactly they're doing! I have a growing appreciation for Max's requirement that his econometrics students build their own functions up from first principles. This is obviously impractical in the long run, but helps to instill a healthy mistrust of others' code. So: caveat econometricus - let the econometrician beware!