This is the fifth in our series of posts by Ph.D. students on the job market this year
Something dramatic happened in Brazilian agriculture between 2007 and 2013: the previously-steady labor intensity of a major crop, sugarcane, fell by 70 percent (see Figure). This drop was the result of the rapid, widespread adoption of mechanical harvesting. My job market paper, “Why Did Sugarcane Growers Suddenly Adopt Existing Technology,” studies how mechanization was achieved.
This is the fourth in our series of posts by PhD students on the job market this year.
Giving livestock to poor households can increase their incomes substantially. This naturally raises the question: why were households not investing in such livestock before? One obvious answer is that they are poor – this means they can neither afford to invest themselves, nor get a loan from a bank (or microfinance organisation). But the puzzle is more subtle than that. When facing a crisis, even very poor households borrow informally, from a network of friends, family, and neighbours, to fund consumption. In addition, households in these networks collectively have the resources needed to invest in livestock. So the real question is: why don’t households pool resources to allow investment? What makes borrowing to invest so different from borrowing to smooth consumption?
This is the second in our series of posts by Ph.D. students on the job market this year
Setting food-price policy is hard. Smallholder farmers are better off with higher crop prices, but consumers want lower prices. So what is a policymaker to do?
Well-integrated agricultural markets can tackle both sides of this food-price policy dilemma, by pulling crops out of surplus areas (to boost prices received by farmers) and pushing food into deficit areas (to reduce prices faced by consumers).
But, alas, agricultural markets in sub-Saharan Africa are not well-integrated. Wide variation in prices across regions and seasons is common, and large gaps between farmer and consumer prices are the norm. There are many possible causes. One issue is that trade is expensive to conduct in the region. To move crops from surplus to deficit areas, agricultural traders must pay high transport costs, spend time and money searching for sellers and buyers, and battle institutional failures like poor credit availability and contact enforcement. Yet, there may be another important driver of the gap between farmer and consumer prices – one that has been voiced by policymakers but is much less well-documented empirically: agricultural traders may be engaging in imperfect competition and extracting rents.
This is the first in our series of posts by Ph.D. students on the job market this year.
One of the key challenges of markets is to assess the quality of goods. A look at online dating websites – a market where information asymmetries loom particularly large - shows different ways in which people try to communicate that they are of “high quality”. A common strategy is to start your introduction with “My friends describe me as…” (to be followed by some glowing testimony “…smart, athletic, high-achieving – yet humble”). Why may this strategy not be effective? It raises questions about whether these friends are truthful and whether they have all the relevant information about your quality as a partner. The really interesting question you never see answered is: “How would your ex-partner describe you?”
My job market paper “The Value of Reference Letters”, coauthored with Rulof Burger (SU) and Patrizio Piraino (UCT), is about the challenges hiring firms face in identifying high-quality applicants. While the literature has largely focused on the role of friends and family members (Topa 2011, Beaman and Magruder 2012) in job referrals, we investigate whether information from ex-employers can facilitate the matching process. Specifically, we test the effect of a standardized reference letter asking previous employers to rate workers on a range of hard skills (e.g. numeracy, literacy) and soft skills (e.g. reliability, team ability).
- Andrew Gelman on how to think more seriously about the design of exploratory studies
- Overcoming premature evaluation discussed on the From Poverty to Power blog “There is a growing interest in safe-fail experimentation, failing fast and rapid real time feedback loops…When it comes to complex setting there is a lot of merit in ‘crawling the design space’ and testing options, but I think there are also a number of concerns with this that should be getting more air time…it can simply take time for a program to generate positive tangible and measurable outcomes, and it maybe that on some measures a program that may ultimately be successful dips below the ‘its working’ curve on its way to that success…more importantly it ignores some key aspects of the complex adaptive systems in which programs are embedded…if we are serious about going beyond saying ‘context matters’ then exhortations to ‘fail fast’ need to be more thoroughly debated.”
- development impact links
We are working on an evaluation of a large rural roads rehabilitation program in Rwanda that relies on high-frequency market information. We knew from the get-go that collecting this data would be a challenge: the markets are scattered across the country, and by design most are in remote rural areas with bad connectivity (hence the road rehab). The cost of sending enumerators to all markets in our study on a monthly basis seemed prohibitive.
Crowdsourcing seemed like an ideal solution. We met a technology firm at a conference in Berkeley, and we liked their pitch: use high-frequency, contributor-based, mobile data capture technology to flexibly measure changes in market access and structure. A simple app, a network of contributors spanning the country, and all the price data we would need on our sample of markets.
One year after contract signing and a lot of troubleshooting, less than half of the markets were visited at the specified intervals (fortnightly), and even in these markets, we had data on less than half of our list of products. (Note: we knew all along this wasn't going well, we just kept going at it.)
So what went wrong, and what did we learn?
- Don’t write a big block of text with no breaks: Whether it is several subheadings, some bullet points or numbered lists, or something else, make the blog post easier for readers to read by using something to break the text up. Remember, readers might be reading this on a mobile phone or skimming it quickly to see if they think it interesting to read, so having 2 pages of solid text with nothing else will not hold reader attention.
- Make sure to give magnitudes, not just significance: don’t just say “we found the program increased education for women”, but tell us by how much, and, where appropriate, some benchmark to help us tell whether this is a big or small effect.
- Hyperlink any references, and spell the authors’ names correctly.
- Get quickly to what you did, and make clear what your methods are: while general motivation for why what you are doing is important is useful, you should be able to make the case for why we should care in a paragraph or less – then we want to hear about what you did, and how you did this. Then give key details – if you do an experiment, make clear the sample sizes, unit of randomization etc.; if you do difference-in-differences, make clear why the parallel trends assumption seems reasonable and what checks you did; if you use an IV, discuss the exclusion restriction and why it seems reasonable; etc.
- Look at previous years for examples: e.g. here is Sam Asher’s, who we hired; here is Mounir Karadja’s explanation of using an IV; and here is Paolo Abarcar’s clear explanation of an experiment he did.
- job market series 2016