【英语财经】非洲经济数据不靠谱 A continent where the numbers game matters

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2016-3-11 23:11

小艾摘要: You might think the job of a statistician is one of the dullest in the world, up there with accounting and chicken sexing. But not in Africa. Yemi Kale is statistician general of the National Bureau o ...
A continent where the numbers game matters
You might think the job of a statistician is one of the dullest in the world, up there with accounting and chicken sexing. But not in Africa. Yemi Kale is statistician general of the National Bureau of Statistics of Nigeria. His task is exhilarating. It is also exhausting.

Mr Kale is not infrequently subjected to threats, particularly when he finds that poverty levels in a certain state are higher than thought. Once, he says, he sent five of his 3,000 workers to collect data from a remote part of Ekiti, in the west of the country. Villagers surrounded the intruders and marched them to the chief, who threatened to kill them. Only intervention from Mr Kale’s headquarters calmed things down.

Mr Kale must be creative. When people are asked how much they earn, suspicion of authority makes them underestimate. Ask them how much they spend, however, and, chest puffed up, they will give a much higher number. In surveys, getting the question right matters.

Mr Kale cannot take much at face value. He even checks his workers’ movements through GPS. Otherwise, staff may be tempted to sit at home and make up the numbers.

It was under Mr Kale that Nigerians woke up one day in 2014 to discover that their economy was 89 per cent bigger than previously imagined, making it Africa’s largest. The overnight “economic miracle” happened after a rebasing of data to better reflect the changing nature of the economy. Booming sectors, such as banking, telecoms and film, which had barely figured in previous calculations, were suddenly revealed to be contributing lavishly to gross domestic product.

Nigeria has one of the most sophisticated statistical operations on the continent. Spare a thought for less fortunate countries where calculating GDP is not much better than guesswork. In his book Poor Numbers, Morten Jerven compares estimates of African GDP for the year 2000 by the World Bank, the University of Pennsylvania and the University of Groningen — three important sources of national income data. They are wildly inconsistent; one ranked Liberia as the second-poorest nation in Africa. Another had it 20 places higher.

Part of the problem is underfunding. When Mr Kale took up the job, he had a budget of $1m, now increased to $5m. Mr Jerven recalls visiting the Central Statistical Office in Lusaka only to find a crop survey delayed because vehicles were not roadworthy. In 2010, he writes, Zambia’s national accounts were being prepared by a single soul.

Even if there were sufficient people to measure it, much economic activity is almost invisible. Neither subsistence farmers, hawkers, itinerant labourers, pickpockets nor prostitutes are likely to be taxed. None will appear, in any meaningful way, in national statistics.

In Zimbabwe, only 6 per cent of the working population is employed in the formal sector, according to the national statistics office. “When you try to read the economy from a conventional view, you totally misread it,” says Patrick Zhuwao, a cabinet member. “There’s so much that’s unrecorded. It’s like trying to use a tape measure to figure out how much Coke is in this glass.”

In 2001, MTN, a South African telecoms company, bid $285m for a mobile licence in Nigeria, based on its estimate that a maximum of 15m people would ever be able to afford handsets. A decade later, there were 80m Nigerian mobile phone subscribers of which MTN, by then making a stonking profit, had 40m. “Where did Nigerians find all this hidden money to buy phones?” asks Miles Morland, a veteran investor in Africa, who argues that official statistics wildly underestimate the continent’s true wealth.

It can go both ways. Last year, Nestlé cut its Africa workforce by 15 per cent after overestimating the size of the market. In Kenya, for example, the company cited numbers estimating a middle class of only about 800,000 in a population of 44m. Most of the would-be middle class in the cities were too poor to afford its products, it found.

Numbers matter to donors, too. They back countries that are doing well and cajole the laggards, b

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