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Natalia Rigol is attempting to figure out if community information can help developing world banks decide who to lend to
Banks in developing countries often won’t lend to the poor, because they have no credit, or they will only lend at prohibitively high rates, making it so that many people can never break out of the cycle of poverty.
Natalia Rigol is a PhD candidate in economics at MIT with an innovative thought. Is it possible, she wonders, to use community information to create an informal credit rating to help banks or microfinance institutions decide who to lend money to? Rigol ran a pilot project asking this question in India this summer, and she is now launching a much larger study of some 1,500 small business owners in poor communities in India.
Tell us a little bit about your background and how you got inspired to become an economist?
I am originally from Cuba, so I lived in Cuba until I was 9 and did the beginning of my schooling there. At the age of 9, I moved to Russia and lived there for two years, and then I was in the Czech Republic for two years. I came to the U.S. when I was 13 and did my middle school to high school in Florida. I went to do my undergrad at Harvard and went for my PhD at MIT where I’ve been for five years. When I was an undergrad, I started working with a mentor—economist Rohini Pande—at Harvard. She’s the one who got me hooked on microfinance and gender issues, which are the things I focus on now.
What’s it like working in India?
The poverty issues in India are extremely striking. India’s a great place [to do research] because it’s a place where a lot of countries are headed. People think of China as being this exemplary country, but India looks a lot more like what poor countries are going to look like soon, in terms of really big income inequality. It’s a place where you can think about poverty issues and really learn.
Tell us about your current project.
One big problem that exists in financing the poor is that, with the poor, you don’t have much information about them. If you think about finance in developed countries, in places like America, you can go to American Express and American Express is going to have reliable information about Natalia Rigol—what her savings look like, what her credit score looks like. A company that’s going to make a loan to Natalia Rigol has a lot of information. But in developing countries there’s nothing like that. In India, they’re only now getting social security numbers for people. A bank doesn’t have much information about poor people. If a bank doesn’t have information about poor people, one way to get a loan is to put up collateral. But of course poor people don’t have that. It’s very difficult for banks to differentiate between Natalia and Emily. We look the same to them. In the end, the bank makes a decision that they’re going to charge a high interest rate, because they’re taking a risk. The question I’m interested in is this: Is there some tool we can develop that can help banks differentiate between Natalia and Emily?
How might that work?
I’ve been thinking about using information that’s available in communities. Especially in a place like India, people live in social networks. It’s not like the U.S. where you live in a house and may not know your neighbors. The project is trying to understand if people have information about one another that a lending institution would find useful in differentiating between Natalia and Emily. I go to a community and ask people to talk to me about Natalia and Emily and tell me different types of information about Natalia and Emily—questions about, for example, work ethic, intelligence, business sense. Who is going to be the most productive? Who is going to grow her business the most? It seems that communities know who’s highly capable.
How does the information-collecting process work?
We first conduct an interview in private for each household in their home. Here we collect a ton of information about a person’s household, business and personal ability. We will use some of this data to validate whether community members know things about one another since it is conducted before anyone knows anything about the fact that they are going to be ranking their peers. We then invite five-member groups [of friends and neighbors] into a hall where they conduct our “ranking game.” Depending on the randomization, they conduct these in the presence of other people or alone, and they are told if their information will be used to allocate grants or not and whether they receive incentives or not. At the end of this game, we conduct a lottery to select the grant winners. We then conduct follow-up interviews to measure changes in business and household wealth and use this data to validate if community members could predict business growth.
Rigol1.jpg
Community members fill out Rigol’s survey. (Natalia Rigol)
What questions do you ask?
At the first interview, we ask for information on the labor activities of all household members, very detailed information about all household businesses, psychometric questions with business owners, and a lot of questions about wealth, health and general well-being.
How do you make sure people tell you the truth about their friends and neighbors?
If you go to a community and ask questions, and people know that the information is going to be used to allocate relatively large grants, it’s possible they’re going to lie. We have lots of pilot data that suggests that people do, in fact, lie if they have an incentive to lie. I want to know how to get people to tell us the truth.
The most salient way to do this is we give people [financial] incentives for their answers. We offer a higher incentive for telling the truth. We use a peer elicitation payment rule, Bayesian Truth Serum, developed by Drazen Prelec here at MIT. The way the rule works is that we ask people their first order beliefs—to rank people from highest to lowest profits—and their second order beliefs—how many people in the community would say that Emily would be ranked the highest? How many would say she would be ranked the second highest, and so on? We pay people based on their first and second order beliefs. Paying for second order beliefs is easy: we see how many people they guessed would rank Emily number one, and then we see how many people did, in fact, rank Emily number one. Paying for first order beliefs is the hard part. The rule works by paying higher amounts to people who give answers that are “surprisingly common,” meaning that the first order belief is more common in the population than people predicted it would be via second order beliefs. Prelec has proven that this incentive payment rule is truthful—people are better off telling the truth about what they know than lying. There are also some lab experiments with students that confirm the properties of this rule.
How much are the grants? And how can these kinds of grants or microloans help people in an impoverished community?
The grants are $100, which is really a massive amount of money for this population. This is about 30 percent of a business owner’s capital. Other studies find that microentrepreneurs are really productive. You give them $100 and their profits increase by 50 percent two or three years down the line and continue to be higher. In terms of impacts: people’s consumption increases, people’s health improves. With $100, your husband can go and get whatever operation and get back to work, while the absence of that $100 means you’re literally in abject poverty.
What are your plans for the future of this project?
We’re doing a baseline survey, and we’ll be done by December or January. Then we’ll randomly allocate grants to measure whether communities were able to predict outcomes or not. We’ll probably track people for one to two years to see the evolution of their businesses and household incomes, and see how community information predicts that. We are working with a microfinance institution, which is very interested in this project. The next step, if it ends up working, would be to see how they could integrate this into their operations.
The price line includes plenty of peaks and valleys, but the sharpest came last September. The line jerked suddenly upward, plateaued at cocoa’s highest price in several years, and then plummeted to its original level. It left an ascending spike of almost perfect symmetry. That spike was Ebola, converted into cocoa prices. (And the most recent drop resulted from declining demand for chocolate.)
Cocoa makes a long and winding journey from bean to bar. The crop starts in the farms of tropical nations, especially in West Africa, and travels through ports, shipping containers and processing plants. But before they can reach your bag of M&M’s, cocoa beans also travel through an intangible dimension—the financial world of price graphs, futures contracts and commodity ETFs. Ever since the New York Cocoa Exchange was founded in 1925, cocoa has been bought and sold in abstract form.
Before we can peek into the financial world of cocoa, a tour of a typical farm. Cocoa requires tropical climate and shady conditions, which means that cocoa farms don’t look much like wheat fields or orange orchards. Trees are grown under a canopy of taller trees, so many farms look like cultivated rainforest. On average, cocoa farms are small operations, around 4 hectares—the size of just 8 football fields. (The average farm in the US, by contrast, is around 95 hectares.) Though cocoa farms can generate relatively big profits, the long-term survival of some farms in question: Recent climate change predictions have made producers nervous, and the world’s largest chocolate manufacturers are at work breeding heat- and drought-resistant trees.
A worker in Brazil inspects cacao beans. (Jochen Weber)
Farm workers who harvest cocoa are, on average, extremely poor, with some below the World Bank poverty line of $1.25 per day. A few years ago, a German photographer Jochen Weber traveled to a Brazilian cocoa farm to take some pictures. He bought some Nutella—the sweetened, hazelnut chocolate spread—as gifts to the farm workers who showed him around. All of them considered it a great luxury, and some had hardly ever encountered the product before. “You can’t work on a cocoa farm not knowing Nutella!” he remembers thinking. One worker, a woman named Leni, found it so delicious that that very day, she finished the entire container. “She said she couldn’t stand it—it was so good.”
Almost all of the world’s cocoa is grown in developing countries and consumed by industrialized countries. The top four producers—Ivory Coast, Nigeria, Ghana and Indonesia—are all in the bottom half of nations by per-capita GDP. More strikingly, the top ten countries ranked by chocolate consumed are all in the top 15 percent. Nine of those countries are in Europe. (In 2012, the United States was ranked 15th.)
It takes a long and complicated supply chain to manage a product that is consumed thousands of miles from where it’s grown. “I always thought of it as this giant river trickling down to these ports,” says John Helferich, who directed research and development for the U.S. division of Mars, Inc. until 2005. In a country like Ivory Coast, small farmers first sell to middle men, who sort and transport big bags of beans to shipping centers like the port city of Abidjan.
From the port cities, global businesses start to dip their fingers in the cocoa jar. Middle men sell to international trading companies like Cargill and ADM, which ship the beans to port cities like Philadelphia and Rotterdam. The cocoa beans are still many steps away from becoming chocolate, but by this point, they’ve entered the financial world.
Commodities traders can participate in the cocoa market in a few ways, but the most common is with cocoa futures. A futures contract is sort of like a rain check: it allows the purchaser to secure a low price. If that price has increased one year later, the trader has a stash of discounted cocoa to resell for a profit. Unlike with a rain check, however, traders suffer the consequences if prices decline. If you buy 1 ton of cocoa futures and the price drops, you’ll be stuck paying last year’s higher price.
Commodities traders can work for food manufacturers, agricultural trading houses, and investment groups like hedge funds. Jonathan Parkman, who works at a trading house called Marex Spectron in London, says the cocoa world is a triangle of interested parties, all making different bets on the prices of cocoa. “Producers want stable high prices,” he explains. “Chocolate makers want stable low prices. The investor wants a trending market without really minding in which direction.”
In other words, farmers want to sell their crop at a premium, while chocolate companies want to get a good deal on their supply. Commodity traders don’t care which side wins, as long as prices rise or fall. (They can make money from falling prices by short-selling futures contracts.)
As with all commodity trading, traders gain an edge by knowing more about market trends than their competition. “A lot of companies will be pod counting, so they’ll go around trees and actually count the number of cocoa pods,” said Emile Mehmet, who is head of bulk commodities at a London-based research agency called Informa. Decades ago, large chocolate producers like Mars would send representatives on pod counting expeditions, in order to spot oncoming low yields in advance. These days, it’s a common enough tactic that it only provides a slight edge. Other sources of cocoa-related intelligence: El Niño predictions, processing figures from cocoa grinders, and the quarterly earnings from big chocolate makers.
Cocoa prices are relatively volatile compared to commodities like corn or wheat. “A lot of the world’s production is concentrated in a small part of the world,” says Mehmet. This means that local forces can have a global impact on prices. For example, the yearly Harmattan trade winds of West Africa can cause a dusty haze that hangs in the air for days, preventing cocoa pods from developing properly. According to cocoa producers in Ivory Coast, that’s the case this year.
A trader who gets wind of news like that can buy cocoa futures, which will rise in value when smaller yields push up the price. In the past, cocoa prices have risen during fungal and insect disease outbreaks. In the future, cocoa prices could rise if climate change shrinks the area where cocoa can feasibly be grown. (On the other hand, climate change could also expand or simply shift cocoa-growing regions.) It’s an awkward truth of commodities markets—and stock markets, too—that traders can make good money from bad news.
Which brings us back to Ebola. Last September, when the disease was spreading rapidly through Liberia and Sierra Leone, traders were closely following the news. If Ebola had spread to cocoa-producing regions, it might have decimated the labor force and interrupted the supply chain. As the graph of cocoa prices shows, investors—like grocery shoppers before a snowstorm—recognized the risk of Ebola and started buying cocoa. Prices spiked, and traders who were ahead of the game raked in some extra cash. When it became clear that Ebola wouldn’t reach Ivory Coast, however, prices returned to their earlier levels.
So what are commodities markets good for, other than making money? Historically, they were created to allow producers to stabilize their supply. Let’s say I sell milk, but my customers complain that milk prices spike every time a blizzard blows through Wisconsin. Futures help me secure a consistent price and sell a more dependable product. That’s why big chocolate makers hire traders: They don’t want their product’s prices to spike and dive along with the price of cocoa. They are why chocolate bars don’t vary much in price – milk, sugar, and cocoa futures keep them consistent.
Over time, though, commodities have grown increasingly abstract, and speculators outside of the chocolate business have gotten in on the game. Some commodities traders might say this is a good thing, if it helps prices take into account real-world problems like weather and disease. John Helferich disagrees. “Commodity traders can sometimes tug around producers and farmers,” he said. A trader might buy cocoa because it’s a relatively better deal than, say, gold. This would increase cocoa prices, whether or not cocoa crops are looking strong.
What traders, producers, and growers would likely agree on is that these days, very few people have a part in every step from bean to bar. Poor farm workers who harvest pods from cocoa trees may never taste produced chocolate—while investors who trade thousands of tons of cocoa may never see a raw cocoa bean. The gift and the burden of globalization is that while all this happens, consumers still get their chocolate.
sumber:https://www.smithsonianmag.com/innovation/could-this-mit-economist-make-banking-useful-poor-180957143/
https://www.smithsonianmag.com/arts-culture/economics-chocolate-180954224
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