World

Can we even measure progress? The state of development data

Imagine you’ve just been appointed Minister of Finance. Tomorrow morning, you must decide where to build schools and clinics, how much to borrow, and which programs to prioritize. To govern well, you need answers to basic questions: How fast is the economy growing? How many children are healthy? How many young people are unemployed?

So what do you find when you open the briefing folders? You are likely to be disappointed. Here are three things that stood out as we wrote this story on development data.

The data is older than you think

For the average low- and middle-income economy, the latest labor force survey is from 2019, the most recent poverty survey is from 2020, and the most recent health survey is from 2015 — more than 10 years ago. In nearly 6 out of 10 low- and middle-income economies, the most recent poverty survey is more than five years old.

In many cases, the absence of timely data means that statistics in areas such as labor markets and public health still portray a pre-COVID-19 world. This can have serious consequences that even modern methods cannot fully offset.  In Nigeria, model-based estimates missed a large rise in poverty. In Ghana, outdated methodologies and data led to a 60 percent revision in GDP.


Hundreds of millions of children are invisible to the data

Around 778 million children aged 5–14 — nearly half of the world’s children in that age range — live in an economy with no recent internationally comparable learning assessment. Without these data, it is hard to know whether or not children are falling behind.


Income matters, but it is not destiny

Strong data systems are possible at all income levels. Mexico has a statistical system that rivals economies with twice its per capita GDP. Burkina Faso, Senegal, Uzbekistan, and the Philippines also stand out as overperformers for their levels of income. Institutions, incentives, and sustained investments matter too.


For a Minister of Finance, in the end, the quality of decisions depends on the quality of the data behind them.

Source : World Bank

Global Business & Finance Magazine

Recent Posts

Buyer beware: a taxonomy of the risks of international carbon credits

As the EU reintegrates international carbon credits into its climate framework, understanding the associated risks…

2 days ago

Green finance or financing green? Bridging the EU’s sustainable-finance and capital-market architectures

The European Union has not done a good job of integrating its green goals with…

2 days ago

Prevention at birth: Birthright citizenship reduces youth crime

When youth crime draws public attention, policymakers typically call for tougher policing and harsher sanctions,…

2 days ago

Trade restrictions, trade policy uncertainty and FDI flows

Trade policy has become a major source of macroeconomic risk. The sharp rise in trade…

2 days ago

Who is most at risk? A global vision indicator to measure climate vulnerability

Nearly 1 in 5 people globally are at high risk from climate hazards, living in…

2 days ago

A Warning Shot for Human Capital: Evidence of an AI Learning Penalty

Are we missing the big story on what AI means for human capital? I raised…

2 days ago