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Why time use data matters for gender equality—and why it’s hard to find

Eliana Rubiano-Matulevich's picture
Photo: © Stephan Gladieu / World Bank

Time use data is increasingly relevant to development policy. This data shows how many minutes or hours individuals devote to activities such as paid work, unpaid work including household chores and childcare, leisure, and self-care activities. It is now recognized that individual wellbeing depends not just on income or consumption, but also on how time is spent. This data can therefore improve our understanding of how people make decisions about time, and expand our knowledge of wellbeing.

Time use data reveals how, partly due to gender norms and roles, men and women spend their time differently. There is an unequal distribution of paid and unpaid work time, with women generally bearing a disproportionately higher responsibility for unpaid work and spending proportionately less time in paid work than men.

How do women and men spend their time?

In a forthcoming paper with Mariana Viollaz (Universidad Nacional de La Plata, Argentina), we analyze gender differences in time use patterns in 19 countries (across 7 regions and at all levels of income). The analysis confirms the 2012 World Development Report findings of daily disparities in paid and unpaid work between women and men.

5 Reasons to Check out the World Bank’s new Data Catalog

Malarvizhi Veerappan's picture

Please help us out by completing this short user survey on the new data catalog.

Data is the key ingredient for evidence based policy making. A growing family of artificial intelligence techniques are transforming how we use data for development. But for these and more traditional techniques to be successful, they need a foundation in good data. We need high quality data that is well managed, and that is appropriately stored, accessed, shared and reused.

The World Bank’s new data catalog transforms the way we manage data. It provides access to over 3,000 datasets and 14,000 indicators and includes microdata, time series statistics, and geospatial data.

Open data is at the heart of our strategy

Since its launch in 2010, the World Bank’s Open Data Initiative has provided free, open access to the Bank’s development data. We’ve continuously updated our data dissemination and visualization tools, and we’ve supported countries to launch their own open data initiatives.

We’re strong advocates for open data, but we also recognize that some data, often by virtue of how it has been acquired or the subjects it covers, may have limitations on how it can be used. In the new data catalog, rather than having such data remain unpublished, we’re making many of these previously unpublished datasets available, and we document any restrictions on how they can be used. This new catalog is an extension of the open data catalog and relies heavily on the work previously done by the microdata library.

Chart: Why Are Women Restricted From Working?

Tariq Khokhar's picture
Also available in: العربية | Français | Español | 中文

Economies grow faster when more women work, but in every region of the world, restrictions exist on women’s employment. The 2018 edition of Women Business and the Law examines 189 economies and finds that in 104 of them, women face some kind of restriction. 30% of economies restrict women from working in jobs deemed hazardous, arduous or morally inappropriate; 40% restrict women from working in certain industries, and 15% restrict women from working at night.

 

How many companies are run by women, and why does it matter?

Masako Hiraga's picture

Happy International Women’s Day! This is an important year to celebrate – from global politics to the Oscars last weekend, gender equality and inclusion are firmly on the agenda.

But outside movies and matters of government, we see the effects on gender equality every day, in how we live and work. One area we have data on comes from companies: what share of firms have a female CEO or top manager?

Only 1 in 5 firms worldwide have a female CEO or top manager, and it is more common among the smaller firms. While this does vary by around the world – Thailand and Cambodia are the only two countries where the data show more women running companies than men.

Better representation of women in business is important. It ensures a variety of views and ideas are represented, and when the top manager of a firm is woman, that firm is likely to have a larger share of permanent female workers.

What data do decision makers really use, and why?

Sharon Felzer's picture

When it comes to revolutions, the data revolution has certainly been less bloody than, say, those in the 18th and 19th centuries. Equally transformative? A question for historians.

AidData, a research and innovation lab located at the College of William & Mary in the US, set out in 2017, to identify what data decision makers in low and middle-income countries use, whose data they use, why they use it, and which data are most helpful.

What can the World Bank learn from AidData’s study, and do data from our own Country Opinion Survey Program, align with AidData’s findings?

Decoding data use: 3500 leaders in 126 low- and middle-income countries.

In 2017 nearly 3500 leaders responded to AidData’s Listening To Leaders Survey (LTL) to help uncover how, when, and why this audience uses information from a range of sources.

This rich data is featured in the report “Decoding Data Use: How do Leaders Source data and Use It To Accelerate Development” and can help any institution target important audiences. For example, what are CSOs and NGOs using most frequently, and for what purpose? How about government respondents? Development partners? The private sector? Does it differ region to region?

Here are some of the key findings:

 

  • Policymakers consult information from the World Bank more than other foreign/international organizations.
  • If you want opinion leaders in client countries to be aware of the Bank’s data and knowledge, bring it to their attention. If you expect them to find it through an internet search, you might be disappointed.
  • Opinion leaders are most likely to regard the knowledge and information helpful if it helps them better understand challenging policy issues and will help them develop implementation strategies in response.
  • Make sure the knowledge and information reflects the local context (be inclusive).
  • Stay focused on policy recommendations to ensure value.

Now let’s see how AidData’s findings compare with the Bank’s Country Opinion Survey Data.

First thing’s first: Accessing data

The AidData survey findings demonstrate that in the world of information and knowledge, decision makers around the world are accessing the Bank’s data.

No Risk, No Reward: The Statistics Netherlands Story

Haishan Fu's picture

Tjark Tjin-A-Tsoi is doing things differently. Before his appointment as the Director General for Statistics Netherlands in April 2014, he was the General Director of the Netherlands Forensic Institute. No doubt that’s why phrases like “actionable intelligence” and forensic analogies about “tracing data” pepper his vision for national statistics in the Netherlands. At a recent presentation here at the World Bank, Tjin-A-Tsoi shared his thoughts on what a modern statistics office looks like, how cognitive science informs data communications, and whether big data will render official statistics obsolete.

A new approach to official statistics

Almost four years after Tjin-A-Tsoi took the helm, Statistics Netherlands has been transformed. It has its own newsroom, a team of media professionals, and employs the latest cognitive science research in its quest to deliver statistical truths to the public. It recently opened a shining new Center for Big Data Statistics, and has an innovation portal for beta products which invites public feedback. One of their current beta products is a Happiness Meter, an interactive infographic that people in the Netherlands can use to calculate and compare their personal happiness score with the rest of the Dutch population.

Surgical care – an overlooked entity in health systems

Emi Suzuki's picture
Also available in: Français | Español | العربية

Five billion peopletwo thirds of world populationlack access to safe and affordable surgical, anesthesia and obstetric (SAO) care while a third of the global burden of disease requires surgical and/or anesthesia decision-making or treatment. Treating the sick very often requires surgery and anesthesia. Despite such huge burden of disease, safe and affordable SAO care is often overlooked.

Why? It may be because surgery and anesthesia are not disease entities. They are treatment modalities that address the breadth of human disease — infections, non-communicable, maternal, child, geriatric and trauma-related disease and injuries, and international development agencies have been focusing on vertical disease-based programs.

Prior to 2015, global data on surgery, anesthesia and obstetric care was virtually nonexistent. With the idea that “We can’t manage what we don’t measure”, the Lancet Commission on Global Surgery developed six Surgical, Obstetric and Anesthesia (SAO) indicators (discussed here) and collected data for them. The analysis of these data show large gaps in SAO care across countries by income groups.

There are 70-times as many surgical workers per 100,000 people in high-income countries compared with low-income countries

The SAO or “surgical” workforce is extremely small in low-income countries (1 SAOs per 100,000 population) and lower middle-income countries (10 SAOs per 100,000 population) whereas there are 69 SAOs per 100,000 population in high-income countries. The discrepancy between high-income countries and low- and middle-income countries is even greater for surgical workforce density than that of physician density.

Measuring surgical systems worldwide: an update

Parisa Kamali's picture
Photo: Chhor Sokunthea / World Bank

Five billion people—two thirds of world population—lack access to safe and affordable surgical, obstetric and anesthesia care with low and middle income countries (LMICs) taking a lead.1-3 Surgical care is a crucial component of building strong health systems and one that is often overlooked (Dr. Jim Kim UHC 2017 video). All people are entitled to quality essential health services, no matter who they are, where they live, or how much money they have. This simple but powerful belief underpins the growing movement towards universal health coverage (UHC), a global commitment under the Sustainable Development Goals (SDGs). Inherent in the framework of UHC is access to safe surgical, obstetric and anesthesia (SOA) care.

An estimated 33 million undergo financial hardship every year from the direct costs of surgical care. And those are the individuals fortunate enough to have access to care.4 Moreover, about 11% of the world’s disability-adjusted life years are attributable to diseases that are often treated with surgery such as heart and cerebrovascular diseases, cancer, and injuries from road traffic accidents.2,5 Other surgically treatable disorders such as obstructed labour, obstetric fistulas, and congenital birth defects are major causes of morbidity and mortality in the developing world.5,7 The delivery of safe and quality SOA care is critical for the realization of many of the Sustainable Development Goals, including: Good health and well-being (Goal 3); No poverty (Goal 1); Gender equality (Goal 5), and Reducing inequalities (Goal 10).

Conversations with Chatbots: Exploring AI’s Potential for Development

Haishan Fu's picture

Development work is getting more technologically sophisticated by the day. The World Bank’s Information and Technology Solutions (ITS) department recently started an Artificial Intelligence (AI) Initiative. At the launch event, we explored the role of AI in development and what it might mean for the work that we do here at the Bank. In short: AI is already here, international organizations have an important role to play, and we need to invest in our skills and expertise.

AI is already being incorporated into development projects

A growing family of Artificial Intelligence techniques are being employed in development. Using machine learning for classification and prediction tasks is becoming as routine as running regressions. Our team recently launched a data science competition on poverty prediction and has been evaluating the performance of different machine learning algorithms. This includes the use of automated machine learning where the machine itself helps to select and tune models in a way a data scientist ordinarily would.

Your Cow, Plant, Fridge and Elevator Can Talk to You (But Your Kids Still Won’t!)

Raka Banerjee's picture
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The Internet of Things (IoT) heralds a new world in which everything (well, almost everything) can now talk to you, through a combination of sensors and analytics. Cows can tell you when they’d like to be milked or when they’re sick, plants can tell you about their soil conditions and light frequency, your fridge can tell you when your food is going bad (and order you a new carton of milk), and your elevator can tell you how well it’s functioning.

At the World Bank, we’re looking at all these things (Things?) from a development angle. That’s the basis behind the new report, “Internet of Things: The New Government to Business Platform”, which focuses on how the Internet of Things can help governments deliver services better. The report looks at the ways that some cities have begun using IoT, and considers how governments can harness its benefits while minimizing potential risks and problems.

In short, it’s still the Wild West in terms of IoT and governments. The report found lots of IoT-related initiatives (lamppost sensors for measuring pollution, real-time transit updates through GPS devices, sensors for measuring volumes in garbage bins), but almost no scaled applications. Part of the story has to do with data – governments are still struggling how to collect and manage the vast quantities of data associated with IoT, and issues of data access and valuation also pose problems.

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