This paper reports on the first randomized evaluation of the impact of introducing the standard microcredit group-based lending product in a new market. In 2005, half of 104 slums in Hyderabad, India were randomly selected for opening of a branch of a particular microfinance institution (Spandana) while the remainder were not, although other MFIs were free to enter those slums. Fifteen to 18 months after Spandana began lending in treated areas, households were 8.8 percentage points more likely to have a microcredit loan. They were no more likely to start any new business, although they were more likely to start several at once, and they invested more in their existing businesses. There was no effect on average monthly expenditure per capita. Expenditure on durable goods increased in treated areas, while expenditures on “temptation goods” declined. Three to four years after the initial expansion (after many of the control slums had started getting credit from Spandana and other MFIs), the probability of borrowing from an MFI in treatment and comparison slums was the same, but on average households in treatment slums had been borrowing for longer and in larger amounts. Consumption was still no different in treatment areas, and the average business was still no more profitable, although we find an increase in profits at the top end. We found no changes in any of the development outcomes that are often believed to be affected by microfinance, including health, education, and women’s empowerment. The results of this study are largely consistent with those of four other evaluations of similar programs in different contexts.

A new working paper (older, ungated copy here) by Duflo et al. The emphasis is mine.

This is consistent with another careful study (link opens a .pdf file) by Crépon et al. of the impact of microfinance in Morocco, where there authors also find that microfinance has no discernible impact on the usual development indicators (i.e., consumption, health, education, etc.)

To be sure, microfinance does appear to have some impacts, as the abstract above indicates — just not the miraculous impacts that are often touted by microfinance advocates.

Agricultural index insurance indemnifies a farmer against losses based on an index that is correlated with, but not identical to, her or his individual outcomes. In practice, the level of correlation may be modest, exposing insured farmers to residual, basis risk. In this article, we study the impact of basis risk on the demand for index insurance under risk and compound risk aversion. We simulate the impact of basis risk on the demand for index insurance by Malian cotton farmers using data from field experiments that reveal the distributions of risk and compound risk aversion. The analysis shows that compound risk aversion depresses demand for a conventional index insurance contract some 13 percentage points below what would be predicted based on risk aversion alone. We then analyze an innovative multiscale index insurance contract that reduces basis risk relative to conventional, single-scale index insurance contract. Simulations indicate that demand for this multiscale contract would be some 40% higher than the demand for an equivalently priced conventional contract in the population of Malian cotton farmers. Finally, we report and discuss the actual uptake of a multiscale contract introduced in Mali.

The article discusses the index insurance contract my coauthors and I have developed for and sold to cotton producer cooperatives in southern Mali. The rest of this post is more technical, as it goes into the details of the two contributions I’ve highlighted above.

Two Contributions

Specifically, the article discusses two things. First, it discusses how compound risk aversion explains the demand for insurance much better than the usual concept of risk aversion. To make things simple, suppose I offer you a 50-50 chance of winning or losing $10, or a lottery whose expected monetary value is zero. I can write this “lottery” as . Plugging this into your utility function  would give your expected utility (where  denote your wealth level before entering the gamble). This would then allow you to compare this to your alternative to taking the gamble, . If , you would take the gamble, and if , you would decline it.

But suppose I were to make things more complicated. Suppose I were to offer you the following compound lottery: (i) a 50% chance of playing a lottery where you have a 50-50 chance of winning or losing $10 (again, this has an expected monetary value of zero), and (ii) a 50% chance of playing a lottery where you have a 25-75 chance of losing $20 and winning $6.67 (this also has a monetary value of zero). Compound lotteries are thus lotteries composed of two or more lotteries. Both this lottery and the previous one have an expected monetary value of zero, but as it turns out, people are considerably more averse to compound lotteries than they are to simpler lotteries (hence the concept of compound risk aversion), a behavior that is related to ambiguity aversion, or Knightian uncertainty.

Second, the article discusses the use of multiscale contracts, or contracts which rely on more than one index. Micro-insurance contracts typically rely on an index and on whether the index has crossed a certain threshold. For example, an insurance will pay out if rainfall (the index) is less than a predetermined level (the threshold).

Here, we use two indices, each with its own threshold: Our insurance pays out if the average cotton yield in one’s cooperative (which is measured very precisely by the parastatal in charge of running the cotton value chain in Mali) falls below a certain threshold and if the average cotton yield in the geographic production zone in which one’s cooperative is located falls below another threshold. By merely introducing this latter threshold, the basis risk to which Malian cotton producers were exposed fell considerably.

Instead of a private response back, I would prefer to see your answer as a post on your blog where you leave the comments open for people to respond for several years unlike many of your other posts where I noticed that the comments sections section was already closed.  I had just finished reading your post about [the inverse farm size--productivity relationship] and was going to leave a comment asking for you opinion about [spam].

Since the comments section was not open, could you please make a seperate (sic) post to express your opinion about it.  And then let me know when that is up, as I try not to subscribe to too many people’s blogs.

Since I receive several such emails every month — many of which address me by my first name and, oddly enough  ask me to post an “infographic” of some sort — I figured it was time I made a public service announcement:

Click here to view the embedded video.

• Comments sections close after two weeks. This is so I don’t have to wade through hundreds of spam comments every morning when I wake up.
• Though I welcome suggestions about what I should be writing about, those suggestions should be about things I am qualified to write about and which will be of interest to this blog’s readership.
• On a related note, this blog is not your personal online marketing space. If that’s what you want, may I suggest that you start your own blog, pay the few hundred dollars it costs to run it every year, and develop your own readership?
• Have a nice day.

I remember there was this fascination with the idea of the informal economy about 10 years ago. Stewart Brand was talking about how brilliant it is that people get by in slums on an informal economy. He’s a friend so I don’t want to rag on him too much. But he was talking about how wonderful it is to live in an informal economy and how beautiful trust is and all that.

And you know, that’s all kind of true when you’re young and if you’re not sick, but if you look at the infant mortality rate and the life expectancy and the education of the people who live in those slums, you really see what the benefit of the formal economy is if you’re a person in the West, in the developed world. And then meanwhile this loss, or this shift in the line from what’s formal to what’s informal, doesn’t mean that we’re abandoning what’s formal. I mean, if it was uniform, and we were all entering a socialist utopia or something, that would be one thing, but the formal benefits are accruing at this fantastic rate, at this global record rate to the people who own the biggest computer that’s connecting all the people.

So Kodak had 140,000 really good middle-class employees, and Instagram has 13 employees, period.

That’s computer scientist Jaron Lanier, who coined the term “virtual reality,” explaining his view that the Internet has destroyed the middle class, in an article on Slate.

Though I’m not sure that the argument that “great stagnation” arguments of the type made by Lanier, which posit that technological change brings increased unemployment, hold much water (thousands of years of technological change seem to indicate otherwise), Lanier’s comment about informal economies is spot on.

Development economists and law-and-economics scholars know the serious inefficiencies that go hand-in-hand with informal economies all too well. Here is one of my favorite articles on those so-called flea-market economies, by Fafchamps and Minten. Here is a whole book by Marcel Fafchamps about the difficulties posed by trying to conduct business in an environment characterized by informality.

a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.

So far, so good. As a development economist, I have to make do with 500 observations more often than not (the largest dataset I have ever worked with had about 8,000 observations), so I obviously welcome ever larger datasets.

Kenneth Cukier and Victor Mayer-Schoenberger, the authors of the article, argue that big data introduces three changes in the information landscape:

1. We have much larger datasets, which are often closer to encompassing entire populations rather than being small samples.
2. We have slightly messier data, which are a byproduct of more data being collected much faster.
3. We have to sacrifice the identification of causal relationships and be happy with mere correlations.

Whither Science?

About that third and last point, the authors also write:

Take UPS, the delivery company. It places sensors on vehicle parts to identify certain heat or vibrational patterns that in the past have been associated with failures in those parts. In this way, the company can predict a breakdown before it happens and replace the part when it is convenient, instead of on the side of the road.The data do not reveal the exact relationship between the heat or the vibrational patterns and the part’s failure. They do not tell UPS why the part is in trouble. But they reveal enough for the company to know what to do in the near term and guide its investigation into any underlying problem that might exist with the part in question or with the vehicle.

That’s great news for UPS, but I can see two problems with this. First, without the identification of causal relationships, there can be no science, social or otherwise. This means that no matter how large a dataset, if it does not allow answering questions of the form “Does X cause Y?,” that dataset is worthless to scientists.

Sure, the dataset can be used for forecasting, much like UPS does. But UPS was never in the business of identifying causal relationships to begin with. Rather, UPS’ purpose is to maximize its profits — to make big money.

Click here to view the embedded video.

This brings me to my second point: There is a fundamental difference between estimating causal relationships and forecasting. The former requires a research design in which X is plausibly exogenous to Y. The latter only requires that X include as much stuff as possible.

When it comes to forecasting, big data is unbeatable. With an ever larger number of observations and variables, it should become very easy to forecast all kinds of things, from election results to sports scores and from stock prices to terrorist attacks.

But when it comes to doing science, big data is dumb. It is only when we think carefully about the research design required to answer the question “Does X cause Y?” that we know which data to collect, and how much of them. The trend in the social sciences over the last 20 years has been toward identifying causal relationships, and away from observational data — big or not.

Some economists argue that ensuring people have titles to their land can ensure a feeling of security and boost production. … The greatest proponent of the argument is Hernando de Soto, a development economist who has managed to win praise from the likes of Bill Clinton and the libertarian Cato Institute.

There is plenty of evidence that land rights are connected to productivity, but new research out of Madagascar shows that it is not always the case.

Duke University researcher Marc F. Bellemare tested whether the land rights component of a $100 million Millennium Challenge Corporation (MCC) compact with the government of Madagascar. He found that the provision of formal land rights, meaning land titles, had not measurable impact on productivity when comparing farmers that did and did not benefit from the MCC compact.

Holding a land title is not sufficient if structures are not in place to enforce land ownership and dole it out.

From a very nice article by Tom Murphy on Humanosphere, which discusses the policy implications of my forthcoming Land Economics article on land rights in Madagascar.

The only correction I would like to make to the article is that the analysis is prospective, meaning that I use data that precedes the MCC’s land titling program. Put differently, I do not evaluate the impact of the MCC’s land titling program and, as far as I can tell, no one has.

Otherwise, the article is spot on: Having a piece of paper that declares you are the owner of a parcel of land does not necessarily make it so. The effectiveness of land titles is predicated on there being an authority to enforce the land rights that supposedly flow from those titles.

How many of us read a story of disaster striking people half a world away and respond by getting out our checkbooks?  Tens of millions of us in any given year, and Americans are especially generous. Relief agencies received more than $1.2 billion in the wake of the disastrous 2010 earthquake in Haiti and $3.9 billion following the 2004 Indian Ocean tsunami.  But is anyone foolish enough to go to the local grocery store, buy food and ship it to communities devastated by disaster? Of course not. That would cost much more, take too long to reach people in need, risk spoilage in transit, and likely not provide what is most needed.

Yet with only minor oversimplification, this is precisely what our government’s food aid programs have done since 1954. Our main international food aid programs are authorized through the Farm Bill and must purchase food in, and ship it from, the United States. This system was originally designed to dispose of surpluses the government acquired under farm price support programs that ended decades ago.  These antiquated rules continue today thanks to political inertia in Washington.

As a result, only 40 cents of each taxpayer dollar spent on international food aid actually buys the commodities hungry people eat; the rest goes to shipping and administrative costs. And the median time to deliver emergency food aid is nearly five months. We can do better.

From a longer piece by my friend and frequent coauthor Chris Barrett on CNN’s Global Public Square blog. Chris is also the author with Dan Maxwell of what is without a doubt the best book anyone can read on food aid.

It’s that time of the year again, when graduate students who are about to enter their final year in economics and related disciplines are getting ready to go on the job market.

Going on the job market is a harrowing experience for most people, however, so I thought I should help job-market candidates by sharing my advice.

This post is the first in a series of three. Today, I’d like to discuss what you should be doing the summer and fall before you go on the job market. The next installment will be posted in the fall and will cover ASSA interviews.

Before Interviewing at ASSA

1. Your number one priority at this time should be finishing and polishing your job-market paper (JMP). This isn’t so much because search committees will read your JMP closely when trying to select candidates to interview but because once the academic year starts, you will realize that being on the job market is a job in and of itself. The more complete your JMP by the time the academic year starts, the less you’ll have to worry about it during the year, and the more time you’ll have to devote to other things. Perhaps more importantly, the more complete your JMP by the time the academic year starts, the more time you have to fix the potential mistakes it contains and to incorporate the comments you receive on it.
2. The best JMPs usually combine a theoretical model with an empirical section that tests the implications of that model. Even if you are working on an empirical question resting on a well-known theoretical framework, it never hurts to have a (perhaps simplified) version of that model in your paper. Including both theory and empirics in your JMP shows your breadth.
3. You should also spend part of your summer securing good letters of recommendation from faculty who know you and your work. The way to do this is by asking faculty “Would you be willing to write me a good letter of recommendation when I go on the job market?” Do not omit the word “good” from your request! When someone hems and haws about it, it means they are not comfortable with writing a good letter of recommendation. Ask someone else.
4. Your recommenders should include the chair of your dissertation committee. If you’ve had a falling out with your advisor and they are not one of your recommenders, you have no choice but to address this in your cover letter. When you do address it, you need to be very careful. For most institutions, but especially for liberal arts colleges, one or more of your references will need to discuss your teaching skills.
5. Your job-market packet should contain at a minimum (i) your cover letter, (ii) a copy of your curriculum vitae (CV), and (iii) a copy of your JMP. It never hurts to include up to two additional samples of your work, especially if you have publications. Some people think that you should not include additional works because of diminishing returns. I think that’s just silly: even if there is a drop in quality between your JMP and your other papers, the benefit from showing that you have a pipeline exceeds that cost.
6. Your cover letter should be about one and a half single-spaced pages. In it, you should explain which job you are applying to, where you saw the advertisement for it, and why you are applying. Then, use section headers to (i) explain what your research agenda is about and how you fit the position, (ii) discuss your teaching experience, and (iii) discuss anything else that is relevant to the position, such as grantsmanship or service. When applying to liberal arts colleges, (ii) should come before (i). If you have family in the area, you should mention it as a reason for applying, as this can make a difference at the margin, especially for universities located in less-than-desirable places.
7. Some universities will also ask for a copy of your teaching evaluations, others for a statement of your teaching philosophy, and others still for a statement of your research philosophy. If you don’t know how to write those, ask your advisor. Or you can read this for inspiration.
8. Make a spreadsheet of all the jobs you plan on applying for so as to track deadlines, requests for letters of recommendation, what each job requires in terms of documents, etc.
9. Don’t limit yourself just to ASSA. If you are interested in agricultural and applied economics department, there are interviews held at the AAEA meetings in August. If you are interested in working in Canada, the CEEE is held in December in Toronto.
10. You should apply for every job which you would be willing to take over unemployment, even those “Any Field” positions and those for which you are a bit of a stretch. The first time I went on the job market, I applied for 72 jobs. I limited myself only to academic jobs, though. If you would be willing to work abroad, apply for those jobs, too. The job market is tough. This is especially true for folks who did not attend a top program in their field.
11. Make sure you give your recommenders at least two weeks to write each letter. Ideally, you should give them one month. Unfortunately, I myself have not always followed that advice.
12. Contrary to popular belief among job-market candidates, search committees do not read JMPs all that closely at this stage of the game. They mostly want to see that you have a JMP that looks it could be presented in a seminar.
13. When you apply for a job at a given institution, ask your recommenders and whoever you are on good terms with on the faculty whether they know anyone at that institution. If they do, ask them to pick up the phone and call their friend to talk you up. Though a phone call from your advisor will not get you a job, it can get you a foot in the door, and that’s really what matters at this point.
14. You will face rejection. You will get rejected several times. This is especially likely at places where you or your recommenders do not know anyone, and for jobs for which you are not an obvious fit. Get used to the idea, and don’t take those rejections personally.
15. Once the calls start coming in to schedule ASSA interviews, be nice to everyone you are in contact with. Whether in your emails or in phone conversations, err on the side of being too nice. Administrative assistants are often asked for their opinion as to whether you were nice. Don’t underestimate their power in the departments you want to work for.

 Contrary to widespread consumer belief, organic farming is not the best way to farm from an environmental point of view. The guiding principle of organic is to rely exclusively on natural inputs.  That was decided early in the 20th century, decades before before the scientific disciplines of toxicology, environmental studies and climate science emerged to inform our understanding of how farming practices impact the environment.  As both farming and science have progressed, there are now several cutting edge agricultural practices which are good for the environment, but difficult or impossible for organic farmers to implement within the constraints of their pre-scientific rules.

From a fascinating post by plant pathologist S.D. Savage, in which he gives six reasons why organic agriculture is not the most environmentally friendly way to farm.

People interested in food policy (PPS590 students, even though your term paper is due tonight, this means you) should read Savage’s post in full, but if you are in a hurry, here are those six reasons:

1. Sub-optimal pesticide use
2. A very high carbon footprint for compost
3. Practical barriers to no-till farming
4. Difficulties with optimized fertilization
5. Lower efficiency of land use, and
6. Lack of a coherent economic model

Savage concludes with the following message for consumers who want to do the right thing:

Between rigorous, science-based regulation, public and private investments in new technology development, and farmer innovation, modern agriculture has been making excellent environmental progress. That trend, not organic, is what we need to encourage.

And with that, Savage’s blog Applied Mythology (see his “About” page for an explanation of the name) goes into my RSS reader and on my blogroll.

From the restaurant scene in which tipping is discussed at length in Reservoir Dogs to Calvin Candie’s seeming addiction to sweets in Django Unchained and from Big Kahuna Burgers (“The cornerstone of any nutritious breakfast!”) in Pulp Fiction to the apple strudel in Inglourious Basterds, it’s all there:

Click here to view the embedded video.