How does commodity price volatility affect the welfare of rural households in developing countries, for whom hedging and consumption smoothing are often difficult? And when governments choose to intervene in order to stabilize commodity prices, as they often do, who gains the most? This article develops an analytical framework and an empirical strategy to answer those questions, along with illustrative empirical results based on panel data from rural Ethiopian households. Contrary to conventional wisdom, we find that the welfare gains from eliminating price volatility are increasing in household income, making food price stabilization a distributionally regressive policy in this context.
That’s the abstract of an article Chris Barrett, David Just, and I have been working on since 2007, and which has just been accepted for publication by the American Journal of Agricultural Economics.
In this article, we ask the question: What is the effect on rural households of increasing the uncertainty (i.e., volatility) surrounding the prices of the staple crops they produce and consume, holding the levels of those same prices constant? In other words, we isolate the impact of an increase in the variance of a price distribution holding the mean of that price distribution constant, and we look at the effects of the covariance between each pair of prices, since a price never varies alone.
This article, I think, is my best piece of research so far, and it is not without reason that I used it as my job-market paper this year. It really has everything one wants one’s research articles to have: Continue reading →
When I took his graduate class on the microeconomics of development, Chris Barrett mentioned that “Heterogeneity and the Three ‘Nons’” differentiate developing economies from industrialized economies:
Heterogeneity: Heterogeneity of endowments, preferences, technologies, and abilities affect outcomes,
Non-Separability: Households are both production and consumption units, and these two decision are not always separable,
Non-Anonymity: Village life is not anonymous, and who one transacts with often affects the terms of exchange,
Non-Market Institutions: High transactions costs often cause households not to participate in markets and to develop seemingly inefficient institutions.
In a post titled “The Transformation Process of Rural Societies,” Frankfurt-based Chilean economist Dany Jaimovich discusses a cool new paper of his which gets at #3 above, i.e., how one can move from non-anonymous to relatively more anonymous transactions: Continue reading →
Given the popularity of my post on the trading game earlier this semester, I thought I should discuss another experiment I run in my principles of microeconomics class to get them to learn the rudiments of economics. This one is even simpler to run–all you need is a deck of cards.
Once again, I didn’t invent anything, as I rely on Charles Holt’s instructions for a market experiment, which you can find here (pages 2 to 5; link opens a .pdf document).
The idea is pretty simple: I split the class into two groups (i.e., buyers and sellers), and I assign each buyer a willingness to pay (WTP) and each seller a willingness to accept money (WTA) for one unit of some imaginary commodity. Those values must be kept secret.
Once students have been assigned to buyers or sellers and have received their valuation for the imaginary commodity, I tell them that they have five minutes to go out there and try to find the best deal for themselves.
For example, if a student is a buyer whose WTP is $5, she should only accept trading offers from sellers whose WTA $5 or less. Indeed, if she meets a seller whose WTA is $6, the minimum acceptable price for that seller exceeds the buyer’s maximum acceptable price, and no trade occurs.
Ideally, a buyer should seek to pay the lowest possible price, and a seller should seek to receive the highest possible price.
For example, if our buyer whose WTP is $5 meets a seller whose WTA is $2, the buyer wants to pay a price that is as low as possible, and the seller wants to receive a price that is as high as possible. Given their individual valuations, however, the only range of price for which a trade is possible is the $2 to $5 range.
Valuations have to be kept secret all along so as to make sure that trading parties do not take advantage of one another. This is like when you buy a house: You don’t start with your highest offer, and you make sure not to reveal your true valuation to the seller to make sure that you pay as little as possible for the house.
What’s in a Price?
Here is the really important part of the experiment, however: As trades occur, the price at which each trade occurs is recorded by the experimenter and announced to the whole class. This is so remaining buyers and sellers know what price is feasible. In other words, to simulate the fact that on most markets, prices are known and serve as a useful signal to market participants.
When I run that experiment, we usually play three to five rounds of the game. For each new round, I assign students to a group different from the one they were previously assigned to, and I give them a new valuation.
If you use a specific valuation structure, it is relatively easy to plot what supply and demand would look like. I use the exact valuation structure found in Holt’s instructions, so my experimental market should look like this:
And lo, we usually hit the $5- to $7-price range as early as the first round!
Once we are done trading, I show students the graph above to show them that one could predict their (aggregate) behavior. This is usually when students truly get how markets operate, and how prices are determined by the interplay between buyers’ WTP and sellers WTA for a commodity.
Next year, I am thinking of tweaking the experiment by making it incentive compatible by giving students one unit of something relatively cheap (e.g., candy) for each unit of surplus they get. Thus, if our buyer whose WTP is $5 meets a seller whose WTA is $2 and they agree on a price of $3, the buyer would get two pieces of candy (WTP – Price = $5 – $3 = $2), and the seller would get one piece of candy (Price – WTA = $3 – $2 = $1).
Anecdotally, one would be tempted to infer the existence of a strong positive relationship between higher food prices and poverty. After all, it is the poor who spend a higher share of their food on basic staples and have the least means to buy food with their meager income. And several studies using the available, imperfect data tend to confirm that relationship.
This is despite the fact that three quarters of poor people live in rural areas and the majority of them earn their living from farming. Some poor farmers produce more food than they consume and hence benefit from higher prices, but many others are net buyers of food and hence lose out when food prices rise. But identifying which households gain and which lose, and hence the overall impact on poverty, requires knowledge of this relationship for all vulnerable households. A major problem is that we still lack the data for accurately gauging who, for a given level of production and pattern of food consumption and purchases, is more likely to be negatively impacted by higher food prices.
From a post by Gero Carletto over at the Development Impact blog.
This is a point that is too often forgotten by nonexperts when discussing the effects of high food prices: that rising food prices (much like food price volatility) generates winners and losers. Continue reading →
At least, that is the answer my coauthor Ken Lee and I come up with in a new article titled “Look Who’s Talking: The Impacts of the Intrahousehold Allocation of Mobile Phones on Agricultural Prices,” forthcoming in the Journal of Development Studies.
More specifically, in a sample of onion farmers in the Philippines, we look at whether there is a statistically significant relationship between whether anyone in a household owns a mobile phone and the price received by that household for its onions.
Failing to find any statistically significant association between the two, we then look at whether there is a statistically significant relationship between whether (i) the household head owns a mobile phone, (ii) the household head’s spouse owns a mobile phone, or (iii) any of the children in the household own a mobile phone and the price received by that household for its onions. Continue reading →