Photo: User 377053 | Pixabay
The Argentinian presidency of the G20 opens this month and will be marked by a focus on infrastructure investment. The G20 and Organisation for Economic Co-operation and Development (OECD) have already announced a widescale data collection initiative to create benchmarks to monitor the risk-adjusted financial performance of private infrastructure debt and equity investments.
It’s about time.
Investors have hit a roadblock when investing in infrastructure. Until now, none of the metrics needed by investors were documented in a robust manner, if at all, for privately held infrastructure equity or debt. This has left investors frustrated and wary. In a 2016 survey of major asset owners by the EDHEC Infrastructure Institute (EDHECinfra) and the Global Infrastructure Hub, more than half declared they did not trust the valuations reported by infrastructure asset managers. How, under such conditions, can the vast increases in long-term investment in infrastructure by institutional players envisaged by the G20 take place?
The first time a World Bank education team tried classroom observations in Brazil, it nearly provoked a state-wide teachers’ strike. It was October 2009 in the northeast state of Pernambuco and two members of the team, Barbara Bruns and Madalena Dos Santos, had handed out stopwatches to school supervisors newly trained in using the Stallings “classroom snapshot” method to measure teacher activities.
Two days later, the stopwatches were on the front page of Pernambuco’s leading newspaper: the teachers’ union called for a state-wide strike to protest an evaluation tool they dubbed the “Stalin method.”
“I thought the grant money we had used to train observers was down the drain,” recalled Bruns, a World Bank retiree now a visiting Fellow at the Center for Global Development. “But the governor, Eduardo Campos, was unfazed. He publicly declared: ‘No one is going to stop me and my secretariat from going into public schools to figure out how to make them better.’ The union backed down and the fieldwork went ahead.”
Despite that several countries have made a call of action for enhancing data collection and capacity building of the national statistical systems to improve migration data, there has not been much progress. The High Level on International Migration in 2013 “emphasized the need for reliable statistical data on international migration, including when possible on the contributions of migrants to development in both origin and destination countries.”
Online pundits, hurried journalists and policymakers love precision. They crave numbers. Preferably exact numbers; ranges suggest uncertainty and make them anxious. As a result, they will love the World Poverty Clock (WPC), a new website that claims to track progress towards ending global poverty in real time (see also this blog and Financial Times article). The website tells you that 632,470,507 people are currently living in extreme poverty - or were, on December 6 at 10:00am… Even more amazingly, the site claims to forecast poverty at any point in the future until 2030, the deadline for the UN’s Sustainable Development Goals. By scrolling along the elegant timeline on the bottom of the WPC screen you will learn, for example, that in 2028, 459,309,506 people will be living in extreme poverty!
Message from Gero Carletto (Manager, LSMS)
A few weeks ago, I attended a meeting of the Committee for the Coordination of Statistical Activities (CCSA) in Muscat, Oman, where I joined a panel discussion on how global survey initiatives like the LSMS or Multiple Indicators Cluster Survey (MICS) can help us measure and monitor many of the SDG indicators. We also discussed how global initiatives like the UN Statistical Commission’s Inter-Secretariat Working Group on Household Surveys (ISWGHS) can help coordinate these efforts and position the household survey agenda within the global data landscape. Everyone seems to agree that monitoring more than 70 SDG indicators will require high-quality, more frequent, and internationally comparable household surveys. Yet, the narrative on household surveys continues to be lopsided. In my view, this is partly because strengthening traditional data sources like surveys and censuses is seen as outmoded and ineffective when compared with the more glittering promises offered by alternative data sources like Big Data.
At the risk of sounding like a luddite, I believe that it’s important for countries and donors alike to continue investing in household surveys to both validate and add value to new types of data. In many of the countries we work in, leapfrogging to the digital revolution without having gone through an analog evolution may be an ephemeral proposition. This in no way means that we should continue doing things the same way: during the past decade, household surveys have evolved dramatically, increasingly relying on technological innovation and new methods to make survey data cheaper, more accurate, and more policy relevant. Methodological and technological innovation remains at the core of the LSMS’s raison d’être and, together with our partners, we will continue pushing the frontier. Until more robust and fully validated alternatives materialize, household survey critics may want to recall the old saying, “Can’t live with ‘em, can’t live without ‘em!”
Conflict and violence are shrinking the space for development at a time when donors are scaling up their presence. To reconcile the conflicting objectives of staff safety with a need to do more (or a greater volume of investment), and doing it better (through higher quality projects), many development workers have started to rely on third party monitoring by outside agents, an approach that is costly and not always effective.The case of Mali demonstrates that alternatives exist.
Less than a decade ago Bank staff could travel freely around in Mali, even to the most remote communities in the country. But today, a mix of terrorism and armed violence renders field supervision of projects impossible in many locations.
To address this challenge—and in the wake of the 2013/14 security crisis in northern Mali—a monitoring system was designed that is light, low cost, and suited for monitoring in insecure areas, but also problem oriented and able to facilitate improvements in project implementation.
So, you are about to start field research in education. Whether you are planning a randomized control trial or a quasi-experiment, hopefully these tips may help!
Devote time and energy towards recruiting and training enumerators (your survey personnel). Someone once said that training enumerators is 95% of the battle in conducting good field research. I would argue that that would be dramatically underestimating its importance. The enthusiasm and perseverance of the enumerators makes or breaks all the hard work that has gone into designing the experiment. And so, in general, devoting at least a week to training them and letting them pilot the tool is essential. I find that reminding enumerators of the higher purpose behind the study really helps as well – in a small way, our shared work is helping improve literacy and numeracy outcomes for children across the world and that’s something that they should rightfully take pride in.
Statistics. Either you love or hate them. We certainly need them to compare and measure data, as well as to make informed decisions. Here at the World Bank, we often get calls from researchers, students and journalists asking for education data: Is there an increase in the number of tertiary education students in Brazil in 2017? How much are governments in South Asia spending on education? Where can we find a database of World Bank education projects?
We try to help answer these, as much as we can, but a quicker and easier way of finding this data is to visit the World Bank’s revamped EdStats website. EdStats – the World Bank’s portal for accessing education-related data – has been around since 1998 and is one of the most used websites by education specialists at the World Bank and partner organizations. User feedback has been highly positive: the interface looks neater, highly mobile and tablet-friendly. Allow me to give you a “tour” of the revamped website.
The data revolution is upon us and the benefits, including improving the efficiency of corporations, spurring entrepreneurship, improving public services, improving coordination, and building profitable partnerships, are becoming more evident.
For public services, the potential gains are impressive. Globally in the electricity sector, an estimated $340 – 580 billion of economic value can be captured by providing more and better data to consumers to improve energy efficiency, and to operators for streamlining project management and the operation of their facilities. Even larger gains ($720 – 920 billion) could be captured in the transport sector.
Exploring the benefits of open data in the solid waste sector has been slower than for other services, however, if you take a closer look, the benefits may be substantial. Solid waste services have a lot to gain – with low service coverage and a lack of modernization in most parts of the world; solid waste services can be costly, representing 10 – 50% of municipal budgets in many developing countries; and it is directly dependent on many actors. To be effective, citizens, institutions, and private companies need to be informed and involved.[Download: What a Waste: A Global Review of Solid Waste Management]
Some examples of what making better quality data available on solid waste services could do include:
The best laid plans… have data. With average waste collection rates of 41% and 68% for low- and lower middle-income countries, respectively, and less than 10% of the corresponding waste disposed in a sanitary manner, many municipalities in the world lack solid waste services. The introduction of modern solid waste systems in these areas represents a monumental organizational change and logistical challenge. It necessitates the introduction of collection services for, among others, each household, and every commercial building and supermarket; the coordination with, informing, and incentivizing all the actors in recycling; the operation of transport services; and the operation of effective disposal or treatment options for the daily, relentless influx of waste. Systematically collecting quality data will help municipalities to undertake strategic planning, integrate service planning into urban planning, and make the necessary decisions that allow them to establish a solid waste system that is properly dimensioned and cost-effective.