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Shared Prosperity: A challenging but important goal to monitor

Judy Yang's picture

Shared prosperity is one of the World Bank Group’s Twin Goals, introduced in 2013. Progress toward this goal is monitored through an indicator that measures the annualized growth rate in average household per capita income or consumption among the poorest 40 percent of the population in each country (the bottom 40), where the bottom 40 are determined by their rank in household per capita income or consumption. Chapter 2 of the 2018 Poverty & Shared Prosperity Report provides an update on the recent mixed progress on shared prosperity around the world in about 2010-15.

The shared prosperity indicator was proposed as a means to shine a constant light on the poorest segments of the population in every country, irrespective of their level of development. Shared prosperity has no target or finish line, because the aim is to continuously improve well-being. In good times and in bad, in low and high-income economies alike, the bottom 40 percent of the population in each nation would be monitored. Tracking the bottom 40’s absolute growth as well as their growth relative to the mean is a way to remind us to always consider distributional impacts and strive for equitable outcomes.

An important but challenging goal to monitor

Despite its importance and universal relevance, shared prosperity is more challenging to monitor than global poverty. While one household survey is sufficient to calculate poverty, shared prosperity measurement requires two recent comparable surveys.

The implication of this stronger data requirement is that 91 out of the 164 economies with an international poverty rate measured in PovcalNet are included in the 6th edition of the Global Database of Shared Prosperity (GDSP).

Data quality in research: what if we’re watering the garden while the house is on fire?

Michael M. Lokshin's picture

A colleague stopped me by the elevators while I was leaving the office.

“Do you know of any paper on (some complicated adjustment) of standard errors?”

I tried to remember, but nothing came to mind – “No, why do you need it?”

“A reviewer is asking for a correction.”

I mechanically took off my glasses and started to rub my eyes – “But it will make no difference. And even if it does, wouldn’t it be trivial compared to the other errors in your data?”

“Yes, I know. But I can’t control those other errors, so I’m doing my best I can, where I can.”

This happens again and again — how many times have I been in his shoes? In my previous life as an applied micro-economist, I was happily delegating control of data quality to “survey professionals” — national statistical offices or international organizations involved in data collection, without much interest in looking at the nitty-gritty details of how those data were collected. It was only after I got directly involved in survey work that I realized the extent to which data quality is affected by myriad extrinsic factors, from the technical (survey standards, protocols, methodology) to the practical (a surprise rainstorm, buggy software, broken equipment) to the contextual (the credentials and incentives of the interviewers, proper training and piloting), and a universe of other factors which are obvious to data producers but usually obscure and typically hidden from data users.

Can our parents collect reliable and timely price data?

Nada Hamadeh's picture
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During the past few years, interest in high-frequency price data has grown steadily.  Recent major economic events - including the food crisis and the energy price surge – have increased the need for timely high-frequency data, openly available to all users.  Standard survey methods lag behind in meeting this demand, due to the high cost of collecting detailed sub-national data, the time delay usually associated with publishing the results, and the limitations to publishing detailed data. For example, although national consumer price indices (CPIs) are published on a monthly basis in most countries, national statistical offices do not release the underlying price data.

 
Crowd sourced price data