I spent part of last with some of the top economists from different parts of the World in Bellagio, Italy.
Our task was to find the means of leveraging Big Data to measure economic progress better by using new tools from the World Data Lab (WDL).
For many years, some economists have argued that the gross domestic product (GDP) has never been a good measure for living standards across the world.
GDP has been used since 1934 to measure the economic performance of a country simply because it is determined by the market value of all the final goods and services.
At the same time, while incomplete, GDP provides a simple and understandable measure of income. With new methods, especially Big Data, we can now refine the GDP estimates so that we enable nations and citizens to make better decisions to help their people move out of poverty.
With new methods and algorithms, it is now possible to provide estimates on how long you are likely to live and what income trajectory you should expect.
With such data, a country can change policies to accelerate change that could impact life expectancy and the future earnings of an individual.
For example, data from the World Poverty Clock shows that Nigeria is moving away from meeting its Sustainable Development Goal (SDG) to eliminate extreme poverty by the year 2030. Nigeria needs to reduce poverty by almost 11 people per hour.
By contrast, Nigeria is going backwards. Given low growth, the country’s current policies force about eight people into extreme poverty.
Unless the country comes up with drastic policy interventions, extreme poverty will rise from the current 75 million to 90 million by the year 2030.
Conversely, a country like Ethiopia, with 21 million in extreme poverty and a target escape rate of three people per minute, will, under current projections, effectively deal with poverty by 2030, with only 2.7 per cent of its population in extreme poverty, down from 22 per cent today.
One may ask: Why is it that Ethiopia will be one of the few sub-Saharan African countries likely to meet their SDG number 1 target?
Ethiopia has got it right in many ways. The country is fast industrialising. It is among the two countries in Africa with a clear engagement strategy with China.
No other country on the continent matches Ethiopia’s policy on transfer of knowledge and technology.
Kenya, in its Vision 2030, set the target of at least 10 per cent GDP growth per year but has never achieved that target.
Ethiopia’s GDP growth rate has remained in the two-digit range for the past couple of years.
For Kenya to meet its SDG goal, it must improve and reach a target escape rate of around 1.5 persons per minute from the current one person per an hour.
It is not difficult for Kenya to surpass such a target rate, since we know that electioneering affects the country’s growth trajectories.
Every election period, we have to start all over and decelerate as we approach another election.
There are also low-hanging fruits, like improving the countries’ productivity.
Much has happened with public-sector reforms, but the country still is unproductive in key sectors like agriculture. In industry, Kenya has done more talking than acting.
The joy of using these emerging tools is that the analytics can be devolved to visualise the performance of the country at the sub-national level.
For example, according to my preliminary research with the World Data Lab, for two people that get out of poverty in Meru County every hour, at least one person is sliding into poverty in Nyeri County in the same period.
The impact of these variances will continue to be felt into the year 2030 unless there are drastic changes.
Meru’s extreme poverty is projected to be below 3 per cent, down from 11 per cent today.
Nyeri’s poverty rate, by contrast, would increase to 37 per cent of the population up from 33 per cent today.
Although the two largely agricultural counties may seem similar, there are significant economic and social differences that affect the well-being of the people.
Hence, they each require different policy interventions. Meru, with a population of more than 1.4 million (based on 2009 census) and an area of 6,936 km², is twice as large in population and size as Nyeri, which covers an area of 3,356 km², and has a population of 661,000 (2009 census).
The counties have topographies characterized by steep ridges and valleys but they differ in environmental preservation.
Due to excessive destruction of forests and riparian lands, Nyeri’s rivers are silting due to soil erosion.
This, coupled with excessive land sub-division, has lowered productivity in the county compared with neighbouring Meru.
Incomes in Nyeri are falling, leading to more people falling into extreme poverty.
On the social front, Nyeri is going through some crisis, as Phyllis Muturi noted in her 2015 paper published in the International Journal of Economics, Commerce and Management: “high school dropout, high rate of early marriages and drug abuse [are] common among the young...increased instability in marriages; increased numbers of single parenthood, increased orphans, female household heads, widows and widowers.”
ESTIMATING LIFE EXPECTANCY
At the individual level, the WDL has another tool to estimate how long the individual will live, www.population.io.
It is the first ever individualised demographic platform for any person in any country in the world, which could also be extended to the sub-national level in the future.
According to Kenya’s official statistics, we already see large differences in life expectancy across the counties.
For example, someone born in Bomet County has a live expectancy of 66 years, 24 years more than an individual born in nearby Homa Bay County and whose life expectancy is 40 years.
These all has to do with risk factors, which, if there are no mitigation measures, can have a far greater impact on the citizens.
Great management expert William Edward Deming once said, “In God we trust; all others must bring data.” In today’s world, we need data at every level to make better decisions and have others trust our intentions.
The writer is an associate professor at the University of Nairobi’s School of Business; Twitter: @bantigito.