Stats Brief by ESCAP: “This Stats Brief gives an overview of big data sources that can be used to produce economic statistics and presents country examples of the use of online price data, scanner data, mobile phone data, Earth Observations, financial transactions data and smart meter data to produce price indices, tourism statistics, poverty estimates, experimental economic statistics during COVID-19 and to monitor public sentiment. The Brief is part of ESCAP’s series on the use of non-traditional data sources for official statistics….(More)”.
How can governments boost citizen-led projects?
Justin Tan at GovInsider: “The visual treat of woks tossing fried carrot cake, the dull thuds of a chopper expertly dicing up a chicken, the fragrant lime aroma of grilled sambal stingray. The sensory playgrounds of Singapore’s hawker centres are close to many citizens’ homes and hearts, and have even recently won global recognition by UNESCO.
However, the pandemic has left many hawkers facing slow business. While restaurants and fast food chains have quickly caught on to food delivery services, many elderly hawkers were left behind in the digital race.
28 year-old Singaporean M Thirukkumaran developed an online community map called “Help Our Hawkers” that provides information on digitally-disadvantaged hawkers near users’ locations, such as opening hours and stall information. GovInsider caught up with him to learn how it was built and how governments can support fellow civic hackers…
Besides creating space for civic innovation, governments can step in to give particularly promising projects a boost with their resources and influence, Thiru says.
Most community-led projects need to rely on cloud services such as AWS, which can be expensive for a small team to bear, he explains. Government subsidies or grants may help to ease the cost for digital infrastructure.
In Thiru’s case, the map needed to be rolled out quickly to be useful. He chose to build his tool with Google Maps to speed up the process, as many users are already familiar with it.
Another way that governments can help is through getting more visibility to these community-led projects with their wide reach, Thiru suggests. Community projects commonly face a “cold start” dilemma. This arises where the community tool needs data for it to be useful, but citizens also hesitate to spend time on a tool if it is not useful in the first place.
Thiru jump started his tool by contributing a few stalls on his own. With more publicity with government campaigns, the process could be sped up considerably, he shares….(More)”.
ASEAN Data Management Framework
ASEAN Framework: “Due to the growing interactions between data, connected things and people, trust in data has become the pre-condition for fully realising the gains of digital transformation. SMEs are threading a fine line between balancing digital initiatives and concurrently managing data protection and customer privacy safeguards to ensure that these do not impede innovation. Therefore, there is a motivation to focus on digital data governance as it is critical to boost economic integration and technology adoption across all sectors in the ten ASEAN Member States (AMS).
To ensure that their data is appropriately managed and protected, organisations need to know what levels of technical, procedural and physical controls they need to put in place. The categorisation of datasets help organisations manage their data assets and put in place the right level of controls. This is applicable for both data at rest as well as data in transit. The establishment of an ASEAN Data Management Framework will promote sound data governance practices by helping organisations to discover the datasets they have, assign it with the appropriate categories, manage the data, protect it accordingly and all these while continuing to comply with relevant regulations. Improved governance and protection will instil trust in data sharing both between organisations and between countries, which will then promote the growth of trade and the flow of data among AMS and their partners in the digital economy….(More)”
The Ancient Imagination Behind China’s AI Ambition
Essay by Jennifer Bourne: “Artificial intelligence is a modern technology, but in both the West and the East the aspiration for inventing autonomous tools and robots that can think for themselves can be traced back to ancient times. Adrienne Mayor, a historian of science at Stanford, has noted that in ancient Greece, there were myths about tools that helped men become godlike, such as the legendary inventor Daedalus who fabricated wings for himself and his son to escape from prison.
Similar myths and stories are to be found in China too, where aspirations for advanced robots also appeared thousands of years ago. In a tale that appears in the Taoist text “Liezi,” which is attributed to the 5th-century BCE philosopher Lie Yukou, a technician named Yan Shi made a humanlike robot that could dance and sing and even dared to flirt with the king’s concubines. The king, angry and fearful, ordered the robot to be dismantled.
In the Three Kingdoms era (220-280), a politician named Zhuge Liang invented a “fully automated” wheelbarrow (the translation from the Chinese is roughly “wooden ox”) that could reportedly carry over 200 pounds of food supplies and walk 20 miles a day without needing any fuel or manpower. Later, Zhang Zhuo, a scholar who died around 730, wrote a story about a robot that was obedient, polite and could pour wine for guests at parties. In the same collection of stories, Zhang also mentioned a robot monk who wandered around town, asking for alms and bowing to those who gave him something. And in “Extensive Records of the Taiping Era,” published in 978, a technician called Ma Daifeng is said to have invented a robot maid who did household chores for her master.
Imaginative narratives of intelligent robots or autonomous tools can be found throughout agriculture-dominated ancient China, where wealth flowed from a higher capacity for labor. So, stories reflect ancient people’s desire to get more artificial hands on deck, and to free themselves from intensive farm work….(More)”.
Selecting the Most Effective Nudge: Evidence from a Large-Scale Experiment on Immunization
NBER Paper by Abhijit Banerjee et al: “We evaluate a large-scale set of interventions to increase demand for immunization in Haryana, India. The policies under consideration include the two most frequently discussed tools—reminders and incentives—as well as an intervention inspired by the networks literature. We cross-randomize whether (a) individuals receive SMS reminders about upcoming vaccination drives; (b) individuals receive incentives for vaccinating their children; (c) influential individuals (information hubs, trusted individuals, or both) are asked to act as “ambassadors” receiving regular reminders to spread the word about immunization in their community. By taking into account different versions (or “dosages”) of each intervention, we obtain 75 unique policy combinations.
We develop a new statistical technique—a smart pooling and pruning procedure—for finding a best policy from a large set, which also determines which policies are effective and the effect of the best policy. We proceed in two steps. First, we use a LASSO technique to collapse the data: we pool dosages of the same treatment if the data cannot reject that they had the same impact, and prune policies deemed ineffective. Second, using the remaining (pooled) policies, we estimate the effect of the best policy, accounting for the winner’s curse. The key outcomes are (i) the number of measles immunizations and (ii) the number of immunizations per dollar spent. The policy that has the largest impact (information hubs, SMS reminders, incentives that increase with each immunization) increases the number of immunizations by 44 % relative to the status quo. The most cost-effective policy (information hubs, SMS reminders, no incentives) increases the number of immunizations per dollar by 9.1%….(More)”.
Platform Workers, Data Dominion and Challenges to Work-life Quality
Paper by Mabel Choo and Mark Findlay: “Originally this short reflection was intended to explore the relationship between the under-regulated labour environment of gig workers and their appreciation of work-life quality. It was never intended as a comprehensive governance critique of what is variously known as independent, franchised, or autonomous service delivery transactions facilitated through platform providers. Rather it was to represent a suggestive snapshot of how workers in these contested employment contexts viewed the relevance of regulation (or its absence) and the impact that new forms of regulation might offer for work-life quality.
By exploring secondary source commentary on worker experiences and attitudes it became clear that profound information deficits regarding how their personal data was being marketed meant that expecting any detailed appreciation of regulatory need and potentials was unrealistic from such a disempowered workforce. In addition, the more apparent was the practice of the platforms re-using and marketising this data without the knowledge or informed consent of the data subjects (service providers and customers) the more necessary it seemed to factor in this commercialisation when regulatory possibilities are to be considered.
The platform providers have sheltered their clandestine use of worker data (whether it be from pervasive surveillance or transaction histories) behind dubious discourse about disruptive economies, non-employment responsibilities, and the distinction between business and private data. In what follows we endeavor to challenge these disempowering interpretations and assertions, while arguing the case that at the very least data subjects need to know what platforms do with the data they produce and have some say in its re-use. In proposing these basic pre-conditions for labour transactions, we hope that work-life experience can be enhanced. Many of the identified needs for regulation and suggestions as to the form it should take are at this point declaratory in the paper, and as such require more empirical modelling to evaluate their potential influences in bettering work-life quality….(More)”
The lapses in India’s Covid-19 data are a result of decades of callousness towards statistics
Prathamesh Mulye at Quartz: “India is paying a huge price for decades of callous attitude towards data and statistics. For several weeks now, experts have been calling out the Indian government and state heads for suppressing Covid-19 infection and death figures. None of the political leaders have addressed these concerns even as official data reflects a small fraction of what’s playing out at hospitals and cremation grounds.
A major reason why administrations are getting away without an answer is that data lapses are nothing new to India.
Successive regimes in the country have tinkered and twisted figures as per their convenience without much consequences. For years, the country has been criticised for insufficient and poor quality data relating to a range of topics, including GDP, farmer suicide, and even unemployment…
Before the pandemic started, the most prominent data controversy in India was around the GDP numbers, which the Modi government continuously changed and chopped to cover up the slowdown in economic growth. In 2019, the Modi government also chose not to publish an unemployment data report that showed that joblessness in the country was at a nine-year high in 2017-18. And last year, in the middle of the pandemic, the government said it had no data on the number of frontline workers who had lost their lives to Covid-19 or a list of police personnel fatalities due to the disease.
Experts say that India’s statistical machinery has been deliberately weakened over the past few years to protect various governments’ false claims and image.
“The weakened statistical machinery manifests itself in different ways such as delays and questions about data quality. Also, when the results of a survey don’t suit the government in power, it tries to suppress data. This happened, for instance, with nutrition data in previous governments too,” said Reetika Khera, associate professor at the Indian Institute of Technology (IIT), Delhi.
“Think of the economy as a patient: data captures its pulse rate. If you don’t listen to the pulse, you won’t be able to diagnose correctly, let alone cure it,” she added….(More)”
Resetting Data Governance: Authorized Public Purpose Access and Society Criteria for Implementation of APPA Principles
Paper by the WEF Japan: “In January 2020, our first publication presented Authorized Public Purpose Access (APPA), a new data governance model that aims to strike a balance between individual rights and the interests of data holders and the public interest. It is proposed that the use of personal data for public-health purposes, including fighting pandemics, be subject to appropriate and balanced governance mechanisms such as those set out the APPA approach. The same approach could be extended to the use of data for non-medical public-interest purposes, such as achieving the United Nations Sustainable Development Goals (SDGs). This publication proposes a systematic approach to implementing APPA and to pursuing public-interest goals through data use. The approach values practicality, broad social agreement on appropriate goals and methods, and the valid interests of all stakeholders….(More)”.
Governance Innovation ver.2: A Guide to Designing and Implementing Agile Governance
Draft report by the Ministry of Economy, Trade and Industry (METI): “Japan has been aiming at the realization of “Society 5.0,” a policy for building a human-centric society which realizes both economic development and solutions to social challenges by taking advantage of a system in which cyberspaces, including AI, IoT and big data, and physical spaces are integrated in a sophisticated manner (CPSs: cyber-physical systems). In advancing social implementation of innovative technologies toward the realization of the Society 5.0, it is considered necessary to fundamentally reform governance models in view of changes in social structures which new technologies may bring about.
Triggered by this problem awareness, at the G20 Ministerial Meeting on Trade and Digital Economy, which Japan hosted in June 2019, the ministers declared in the ministerial statement the need for “governance innovation” tailored to social changes which will be brought about by digital technologies and social implementation thereof.
In light of this, METI inaugurated its Study Group on a New Governance Model in Society 5.0 (hereinafter referred to as the “study group”) and in July 2020, the study group published a report titled “GOVERNANCE INNOVATION: Redesigning Law and Architecture for Society 5.0” (hereinafter referred to as the “first report”). The first report explains ideal approaches to cross-sectoral governance by multi-stakeholders, including goal-based regulations, importance for businesses to fulfill their accountability, and enforcement of laws with an emphasis on incentives.
Against this backdrop, the study group, while taking into consideration the outcomes of the first report, presented approaches to “agile governance” as an underlying idea of the governance shown in the Society 5.0 policy, and then prepared the draft report titled “Governance Innovation ver.2: A Guide to Designing and Implementing Agile Governance” as a compilation presenting a variety of ideal approaches to governance mechanisms based on agile governance, including corporate governance, regulations, infrastructures, markets and social norms.
In response, METI opened a call for public comments on this draft report in order to receive opinions from a variety of people. As the subjects shown in the draft report are common challenges seen across the world and many parts of the subjects require international cooperation, METI wishes to receive wide-ranging, frank opinions not only from people in Japan but also from those in overseas countries….(More)”.
Measuring Commuting and Economic Activity Inside Cities with Cell Phone Records
Paper by Gabriel Kreindler and Yuhei Miyauchi: “We show how to use commuting flows to infer the spatial distribution of income within a city. A simple workplace choice model predicts a gravity equation for commuting flows whose destination fixed effects correspond to wages. We implement this method with cell phone transaction data from Dhaka and Colombo. Model-predicted income predicts separate income data, at the workplace and residential level, and by skill group. Unlike machine learning approaches, our method does not require training data, yet achieves comparable predictive power. We show that hartals (transportation strikes) in Dhaka reduce commuting more for high model-predicted wage and high-skill commuters….(More)”.