Is the rhino horn trade a cartel? Economic analysis suggests that it works as a

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A white rhino in Kruger National Park in South Africa, by Rhett A. Butler / Mongabay.

>> Economist Adrian Lopes used data modeling to explore links between rhino horn suppliers in India and South Africa.

>> Its findings suggest a market model in which suppliers from the two countries team up rather than compete with each other, setting a quantity and price that maximizes profits all around.

>> Lopes’ research also indicates that stricter conservation laws can reduce the number of rhinos killed, but corruption and institutional instability can erode those gains.

When economist Adrian Lopes read a recent report by the World Wide Fund for Nature (WWF), a question comes to mind. The report, which described rhino poaching and anti-poaching measures, spoke of the link between countries where rhinos are found and countries where rhino horns are in high demand for their alleged medicinal properties. The economics researcher in him believed there was another piece of the puzzle to explore.

“The report briefly mentions South Africa, Asia and India as the main countries of origin of rhino horns, which have made their way to China and Vietnam,” says Lopes, assistant professor at the American University of Sharjah, United Arab Emirates. “But he was not talking about the link between the source countries.” Lopes decided to look into the matter in an article recently published in the Australian Journal of Agriculture and Resource Economics.

Having already wanted The rise of poaching in Kaziranga National Park in India, he had an idea of ​​where to start looking for answers. Of all the rhino range countries, only India and South Africa have detailed, long-term and up-to-date records of poaching cases, he says. (Nepal also keeps detailed records, but the number of poachings there is too low to provide much data.)

Lopes’ curiosity grew when he compared the graphs of poaching cases in the two range countries. “I noticed that when the number of poaching cases increased in South Africa, a similar peak could be observed in India. Likewise, a fall in India would correspond to a fall in South Africa, ”he told Mongabay.

Given that the illicit trade in rhino horn is essentially an international trade in commodities, Lopes had a hunch that the analytical tools used by economists could help shed light on what was going on.

By examining the trends in the data, he defined the objectives of his study. He wanted to see what the available data suggested about the relationship between suppliers in India and South Africa; develop a model to explore the impact of factors such as the rule of law and corruption; and to see if he could come up with a model that would predict the black market price for rhino horn.

A female black rhino. Around 5,000 members of the critically endangered species remain – still dangerously low, but almost twice as many as in the 1990s, after poaching and habitat loss brought the species to the brink of the wilderness. extinction. Photo by Butler Rhett A / Mongabay.

Collusion between suppliers

Although rhino horn is an internationally traded commodity, it is not just an ordinary commercial good. High barriers to entry – namely, the need for connections and resources to smuggle illegal merchandise without getting caught – mean rhino horn traders do not operate in an open market with competition. perfect. On the other hand, the horns come from several countries on two continents, which means that a complete monopoly is also unlikely.

With this in mind, Lopes chose to test whether the available data on the rhino trade matched a market model known as a “collusive oligopoly”. The best-known example of this type of market is OPEC, the Organization of the Petroleum Exporting Countries, which makes a cartel that together decides how much oil to make available on the world market, and at what price. . Illegal drug cartels are believed to work in the same direction, and studies have found similar patterns in illegal trade in tiger parts in China. “This is the case with rhino horns,” says Lopes.

Lopes built his mathematical model with a few theories in mind. First, he hypothesized that if the rhino horn market indeed functioned as a collusive oligopoly, South Africa would be the market leader due to its much larger rhino population (similar to how Saudi Arabia has historically played the dominant role in OPEC). He also predicted that vendors were unlikely to keep large stocks of horns, due to the dangers and difficulties inherent in storing illegal goods. Instead, he speculated that the number of rhinos killed in a given time period was most likely correlated with the number of rhino horn vendors who believed they could sell during that time.

In addition, he predicted that South African suppliers would set the optimum price and quantity to maximize profits, a calculation taking into account the costs of purchasing rhino horn and smuggling to Asian markets. In turn, Indian suppliers would adjust the quantity and price of the horns they supplied to those same markets, in line with expectations set by South Africa.

Lopes conducted a series of statistical analyzes on this collusive oligopoly model. He found the data to fit well, indicating that his assumptions were valid. What emerged from the data was a picture of a complex, interwoven pattern in which suppliers in South Africa set the price, while suppliers in India act in concert to maximize profits all around.

But Lopes has a word of warning: his research is limited to fitting data points in a model and gives no insight into what such collusion might take. He says he doesn’t intend to suggest, for example, that rhino horn traders around the world hold regular in-person meetings to sort out details. “Given the clandestine nature of rhino poaching, all one can do is theorize and try to find empirical evidence to back up the theory,” he says. “You cannot claim to know exactly how a union handles its criminal operations.”

A large one-horned rhino in Kaziranga National Park in India, home to the largest population of one-horned rhinos in the world. Recent economic analysis suggests that rhino horn traffickers in India and South Africa are getting along rather than competing with each other. Photo by Udayan Dasgupta for Mongabay.

Rule of law versus corruption

Lopes also aimed to explore how external factors such as law enforcement and corruption, as well as changing demand, impact the number of rhinos killed for their horns.

His next step was to try to control these factors in his model. Previous research suggested that the key driver of rhino horn demand was the income of potential consumers in major markets of Vietnam and China (in contrast, price was shown to have relatively little impact on the market. demand, a familiar model for luxury goods). As a result, Lopes took into account data on per capita economic activity in these countries. Likewise, to monitor corruption and governance, Lopes used World Bank indices on institutional quality such as corruption control, rule of law and political stability. To monitor conservation policy, he connected data on penalties imposed on poachers in the two source countries.

Even after controlling for these factors, poaching in India has been found to be positively and significantly related to South African poaching. In particular, there was a strong association between the number of rhinos killed for their horns in South Africa in any given year and the number of rhinos killed the following year in India.

While discussing the controlling factors, Lopes observed that as corruption and institutional instability increased, poaching also increased. In contrast, tougher environmental protection laws and tougher penalties for violating them have been linked to a drop in rhino killings. The interaction of these various factors can be complex. In India, for example, he observed that although conservation policy has become stricter over time, the impact of corruption and instability remains so great that it consistently outweighs the positive effects. conservation policy.

Lopes also sought to trace and predict the prices of rhino horns on the black market. To do this, he used the data he had collected on income in Vietnam and China (as an indicator of demand) and a benchmark price of $ 65,000 per kilogram of rhino horn in 2012, taken from a study by economist Michael ‘t Sas-Rolfes. . The model put forward a figure of $ 69,454 to $ 77,548 per kilogram of rhino horn by 2022.

A white rhino grazes in the Kruger National Park in South Africa. The country is home to around 80 percent of the world’s rhinos and has also been the hardest hit by poachers, with losses peaking at 1,215 animals in 2014. Photo by Rhett A. Butler / Mongabay.

Apply the results

Even in economics, the debate rages on when and how data models can be used to explain and predict real-world phenomena. And applying such research to conservation policy can be even more controversial. “As an economist, I find that people involved in conservation are divided over this work,” says Lopes.

Still, he says he hopes his work can provide law enforcement officials and conservationists with new perspectives and a useful analytical framework.

Conservation policy has traditionally focused on reducing demand and improving anti-poaching strategies. “However, given the growing interconnection of trade and information flows across borders, we propose that conservation policy also focus on the possibility that trade in endangered species can be coordinated across borders – from the source to the end consumer, ”writes Lopes.

He says he’s been invited to several conferences to talk about his findings, and this encourages him to do similar work in the future. “I have seen a growing dialogue between environmentalists and economists. Questions are being asked on both sides and a growing number of economists are getting to work on conservation-related panels.

QUOTE:

  • Lopes, AA (2018). Transnational links in rhino poaching and the black market price of rhino horns. Australian Journal of Agricultural and Resource Economics, 63 (1), 95–115. https://doi.org/10.1111/1467-8489.12286

This article first appeared on mongabay.


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