Last week I attended Stanford University’s Quality of Governance conference, expertly organized by a rising star of the field, Saad Gulzar. I thought I’d follow in the footsteps of Dave Evans and others and summarize the findings of the papers presented. They provide a sketch of the frontier of research on state capacity.
Being from Kolkata, I have always been used to floods. Prolonged flooding typically meant schools and offices closed, traffic jams and a much-needed respite from the tropical summer heat. However, it was during a field visit to the flood prone northeastern border of Bangladesh, where rivers from India flow downstream into Bangladesh, that I fully appreciated the importance of disaster early warning systems and regional collaboration in saving lives, property, enabling communities to evacuate and prepare for extreme weather events.
Disaster early warning systems, along with other information services based on weather, water and climate data (sometimes known as “hydromet” or “climate services”) play a key role in disaster preparedness and improving the productivity and performance of climate sensitive sectors such as agriculture. Along with investments in resilient infrastructure, risk financing strategies and capacity building measures, they are a key part of a toolkit for strengthening disaster and climate resilience. Research shows that for every dollar spent on disaster early warning systems, the benefits range from $2-10. In South Asia, these are particularly important given the region’s extreme vulnerability to climate risks and staggering socio-economic costs arising from extreme weather events.
By 2050, two-thirds of all people will live in cities. Each year, 72.8 million more people live in urban areas. That’s the equivalent of a new San Diego appearing every week.
But By 2030, climate change alone could force up to 77 million urban residents into poverty.
Achieving resilience is the goal – and the good news is that cities aren’t alone on the team.
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.
The International Comparison Program (ICP) team in the World Bank Development Data Group commissioned a pilot data collection study utilizing modern information and communication technologies in 15 countries―Argentina, Bangladesh, Brazil, Cambodia, Colombia, Ghana, Indonesia, Kenya, Malawi, Nigeria, Peru, Philippines, South Africa, Venezuela and Vietnam―from December 2015 to August 2016.
The main aim of the pilot was to study the feasibility of a crowdsourced price data collection approach for a variety of spatial and temporal price studies and other applications. The anticipated benefits of the approach were the openness, accessibility, level of granularity, and timeliness of the collected data and related metadata; traits rarely true for datasets typically available to policymakers and researchers.
The data was collected through a privately-operated network of paid on-the-ground contributors that had access to a smartphone and a data collection application designed for the pilot. Price collection tasks and related guidance were pushed through the application to specific geographical locations. The contributors carried out the requested collection tasks and submitted price data and related metadata using the application. The contributors were subsequently compensated based on the task location and degree of difficulty.
The collected price data covers 162 tightly specified items for a variety of household goods and services, including food and non-alcoholic beverages; alcoholic beverages and tobacco; clothing and footwear; housing, water, electricity, gas and other fuels; furnishings, household equipment and routine household maintenance; health; transport; communication; recreation and culture; education; restaurants and hotels; and miscellaneous goods and services. The use of common item specifications aimed at ensuring the quality, as well as intra- and inter-country comparability, of the collected data.
In total, as many as 1,262,458 price observations―ranging from 196,188 observations for Brazil to 14,102 observations for Cambodia―were collected during the pilot. The figure below shows the cumulative number of collected price observations and outlets covered per each pilot country and month (mouse over the dashboard for additional details).
Figure 1: Cumulative number of price observations collected during the pilot
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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.
Photo: Burst | Pexels Creative Commons
Australia’s involvement in the Global Infrastructure Facility (GIF)—as a founding member, and co-chair of the advisory council over the past year—underscores our commitment to lift investment in global infrastructure, which is a critical component to ensuring economic growth and poverty alleviation.
Strong economic infrastructure underpins human development, enables movement of people and goods, provides access to and expands markets and services, facilitates innovation, and enhances competitiveness.
Photo: Creativa Images | Shutterstock
Critically constrained public resources on the one hand, and huge existing infrastructure needs for basic services on the other, make private participation in emerging markets and developing economies (EMDEs) not just critical, but in fact, imperative. Crowding in private finance is essential to spur economic development and meet the twin goals of shared prosperity and elimination of extreme poverty, as well as to achieve the Sustainable Development Goals.
The Private Participation in Infrastructure (PPI) Database, with data spanning over almost 27 years, has become a powerful tool and measure for gauging the level of private investment in infrastructure in EMDEs.