Generic indicator sets back fight against poverty
Someone asked a thought-provoking question at a meeting this week: “How can Sandton and Alexandra both be upper middle income? If the goal of a development loan is to alleviate poverty, how do both of these areas get the same financing terms and levels of development assistance?”
In the development sphere, financing terms and development assistance are largely tied to a single indicator: gross national income (GNI) per capita. Countries are grouped into an income category, which is based on income per capita. The World Bank has four annual income categories for world economies — low (<$1,026), lower-middle ($1,026-$3,995), uppermiddle ($3,996-$12,375) and high (>$12,375). Many institutions rely on these classifications.
GNI per capita is a function of economic growth, inflation, exchange rates and population. As GNI increases and countries graduate into a new income category, their eligibility for donor support and concessional lending declines or disappears.
SA’s GNI per capita is $5,750 — on the lower end of the upper middle income category. However, its Gini coefficient, a measure of inequality, is 0.63, one of the world’s highest. The World Inequality Database says in SA the top 1% of earners take home nearly 20% of all income, while the top 10% take home 65%. It is safe to say these are high-income earners. But many of the bottom 50% of earners who take home just 6% would crowd into the poorest income classification, earning less than $50 per month or $600 a year.
The World Bank uses GNI per capita to classify income because it is a “useful and easily available indicator” closely aligned with human development indicators such as quality of life, enrolment in schools and life expectancy. But it does not capture inequality, a direct contributor to vastly different outcomes in different demographics in a single country.
The use of GNI per capita for income classification can prevent development assistance from reaching where it is needed most. Though the number of countries categorised as lowincome fell from 63 in 2000 to 31 in 2016, more people live in poverty in middle income countries than in any other income classification.
Donor support, often tied to income classification, is critical for strengthening human capital. If the transition between income categories is not managed effectively, the progress brought by donor resources can slow or, even worse, have an adverse rebound. In Romania the withdrawal of donor funding for HIV-related programming led to a rise in HIV rates among drug users from 3.3% in 2009 to 27.5% in 2013.
Financing and donor grants for development objectives should be tied to the problem that is being addressed rather than to a generic indicator. Though SA is an upper middle income country, it has one of the highest tuberculosis prevalences globally. TB is the leading cause of death — about 89,000 people annually, 10 people every hour. Given that tuberculosis is preventable and treatable, financing and technical assistance are required to get this health epidemic under control.
On the gender front, SA was named the world’s single most dangerous country for women to visit and travel in alone in the Women’s Danger index. Data from the World Economic Forum, World Health Organisation and UN were used to score each country across eight categories, with two — how safe it is to walk alone at night and international homicide of women — being weighted the heaviest. SA was the only country to receive an “F” overall. In 2019 more than 100 rapes were reported daily and more than 2,700 women and 1,000 children murdered by men. Developing safer communities is a critical priority for SA.
Gender-based violence and tuberculosis are representative of just two (of many) areas — gender and public health — in which financing and technical assistance are desperately needed to make SA more liveable. Using indicators relevant to these specific challenges is more likely to ease access to finance and yield development progress compared with a broad GNI per capita approach, which is unable to capture the vast heterogeneities facing many countries.
Ultimately, the use of the GNI indicator may be directly at odds with the quest of development partners to reduce inequality. Undeniably, disaggregating development objectives and tying financing to relevant indicators are a more difficult undertaking. However, in many developing countries, including SA, a reliance on the GNI per capita approach risks widening the inequality chasm.
GNI PER CAPITA DOES NOT CAPTURE INEQUALITY, A CONTRIBUTOR TO VASTLY DIFFERENT OUTCOMES IN A SINGLE COUNTRY