Revisiting the Migration-Development Nexus: A Gravity Model Approach

This paper presents empirical estimates of a gravity model of bilateral migration that properly accounts for non-linearities and tackles causality issues through an instrumental variables approach. In contrast to the existing literature, which is limited to OECD data, we have estimated our model using a matrix of bilateral migration stocks for 127 countries. We find that the inverted-U relationship between income at origin and migration found by other authors survives the more demanding bilateral specification but does not survive both instrumentation and introduction of controls for the geographical and cultural proximity between country pairs. We also evaluate the effect of migration on origin and destination country income using the geographically determined component of migration as a source of exogenous variation and fail to find a significant effect of migration on origin or destination income.Gravity models, international migration, economic growth

Harnessing Innovative Data and Technology to Measure Development Effectiveness

In this study, the authors discuss and show how new kinds of digital data and analytics methods and tools falling under the umbrella term of Big Data, including Artificial Intelligence (AI) systems, can help measure development effectiveness. Selected case studies provide examples of assessments of the effectiveness of ODA-funded policies and programmes. They use different data and techniques. For example, analysis of mobile phone data and satellite images: to estimate poverty and inequality, traffic congestion, social cohesion or machine learning approaches to social media analysis to understand social interactions and networks, and natural language processing to study changes in public awareness. A toolkit contains resources and suggestions on key steps and considerations, including legal and ethical, when designing and implementing projects aimed at measuring development effectiveness through new digital data and tools. The chapter closes by describing the core principles and requirements of a vision of a ‘Human AI’, which would reflect and leverage the key features of current narrow AI systems that are able to identify and reinforce the neurons that help them reach their goals. A Human AI would be a data and machine-enabled human system (such as a society) that would seek to continuously learn and adjust to improve—rather than prove after the facts—the effectiveness of its collective actions, including development programming and public policies

Revisiting the Migration-Development Nexus: A Gravity Model Approach

This paper presents empirical estimates of a gravity model of bilateral migration that properly accounts for non-linearities and tackles causality issues through an instrumental variables approach. In contrast to the existing literature, which is limited to OECD data, we have estimated our model using a matrix of bilateral migration stocks for 127 countries. We find that the inverted-U relationship between income at origin and migration found by other authors survives the more demanding bilateral specification but does not survive both instrumentation and introduction of controls for the geographical and cultural proximity between country pairs. We also evaluate the effect of migration on origin and destination country income using the geographically determined component of migration as a source of exogenous variation and fail to find a significant effect of migration on origin or destination income

Using Facebook advertising data to describe the socio-economic situation of Syrian refugees in Lebanon

While the fighting in the Syrian civil war has mostly stopped, an estimated 5.6 million Syrians remain living in neighboring countries1. Of these, an estimated 1.5 million are sheltering in Lebanon. Ongoing efforts by organizations such as UNHCR to support the refugee population are often ineffective in reaching those most in need. According to UNHCR's 2019 Vulnerability Assessment of Syrian Refugees Report (VASyR), only 44% of the Syrian refugee families eligible for multipurpose cash assistance were provided with help, as the others were not captured in the data. In this project, we are investigating the use of non-traditional data, derived from Facebook advertising data, for population level vulnerability assessment. In a nutshell, Facebook provides advertisers with an estimate of how many of its users match certain targeting criteria, e.g., how many Facebook users currently living in Beirut are “living abroad,” aged 18–34, speak Arabic, and primarily use an iOS device. We evaluate the use of such audience estimates to describe the spatial variation in the socioeconomic situation of Syrian refugees across Lebanon. Using data from VASyR as ground truth, we find that iOS device usage explains 90% of the out-of-sample variance in poverty across the Lebanese governorates. However, evaluating predictions at a smaller spatial resolution also indicate limits related to sparsity, as Facebook, for privacy reasons, does not provide audience estimates for fewer than 1,000 users. Furthermore, comparing the population distribution by age and gender of Facebook users with that of the Syrian refugees from VASyR suggests an under-representation of Syrian women on the social media platform. This work adds to growing body of literature demonstrating the value of anonymous and aggregate Facebook advertising data for analysing large-scale humanitarian crises and migration events

Using Facebook advertising data to describe the socio-economic situation of Syrian refugees in Lebanon

While the fighting in the Syrian civil war has mostly stopped, an estimated 5.6 million Syrians remain living in neighboring countries1. Of these, an estimated 1.5 million are sheltering in Lebanon. Ongoing efforts by organizations such as UNHCR to support the refugee population are often ineffective in reaching those most in need. According to UNHCR's 2019 Vulnerability Assessment of Syrian Refugees Report (VASyR), only 44% of the Syrian refugee families eligible for multipurpose cash assistance were provided with help, as the others were not captured in the data. In this project, we are investigating the use of non-traditional data, derived from Facebook advertising data, for population level vulnerability assessment. In a nutshell, Facebook provides advertisers with an estimate of how many of its users match certain targeting criteria, e.g., how many Facebook users currently living in Beirut are “living abroad,” aged 18–34, speak Arabic, and primarily use an iOS device. We evaluate the use of such audience estimates to describe the spatial variation in the socioeconomic situation of Syrian refugees across Lebanon. Using data from VASyR as ground truth, we find that iOS device usage explains 90% of the out-of-sample variance in poverty across the Lebanese governorates. However, evaluating predictions at a smaller spatial resolution also indicate limits related to sparsity, as Facebook, for privacy reasons, does not provide audience estimates for fewer than 1,000 users. Furthermore, comparing the population distribution by age and gender of Facebook users with that of the Syrian refugees from VASyR suggests an under-representation of Syrian women on the social media platform. This work adds to growing body of literature demonstrating the value of anonymous and aggregate Facebook advertising data for analysing large-scale humanitarian crises and migration events

Empowering society by reusing privately held data for official statistics - A European approach

The High-Level Expert Group on facilitating the use of new data sources for official statistics has been created in the context of the data and digital strategy of the European Commission (EC). The task of the Expert Group is to provide recommendations aimed at enhancing data sharing between businesses and government (B2G) for the purpose of producing official statistics (B2G4S). The Expert Group consists of high-level experts with various backgrounds that are particularly relevant to B2G4S. Businesses generate and use data primarily for business-related purposes. The motivation for B2G4S stems from the high societal value that such privately held data can potentially generate when transformed into reliable, relevant and timely official statistics that are made available to everybody, for free. Transforming data into statistical information requires cooperation between private data holders and statistical authorities. On a voluntary basis there have been many collaborative efforts by businesses and statistical authorities to produce statistics based on privately held data, but for various reasons the use of such data for official statistics is still far below the level required to provide society with the high-quality and timely official statistics it needs in the increasingly data-driven world

Applications and Implications of Big Data for Demo-Economic Analysis: The Case of Call-Detail Records

This dissertation analyzes and discusses various applications and implications of Big Data for demo-economic analysis, focusing on the analysis of cell-phone data collected by telecommunication operators for billing purposes, commonly referred to as call-detail records, or CDRs, which include the time and duration of calls, the location of the emitter and receiver, etc. This is done by placing the resulting opportunities and challenges within the broader context of the 'Data Revolution', presented in Chapter 1. In this context, "applications" refer to ways in which CDR analytics can be used for research and policymaking purposes by leveraging the information contained in these data on human behaviors, for example to predict criminality (Chapter 2), study weather and mobility patterns (Chapter 3), and estimate income levels (Chapter 4) and population density (Chapter 5). "Implications" refer to ways in which CDR analytics has and can be expected to affect and be affected by ethical, political, legal, and institutional factors and processes (Chapter 6). At the heart of Big Data are new kinds of passively emitted digital data or `crumbs' that are the by-product of the fast growing and already near-ubiquitous use of digital devices and services by humans around the globe. These 'crumbs' leave digital traces of most of their actions that are collected and can be analyzed through powerful methods and machines by new types of stakeholders, including multi-disciplinary teams. Chapter 1 analyzes the advent of the Big Data phenomenon over the past decade, with particular attention to its observed and possible effects on social science research and policymaking. In addition to providing an historical overview, it proposes taxonomies and concepts to clarify the nature and significance of the change brought about by Big Data. An important point of the chapter is the distinction it introduces between big data as new kinds of large datasets and Big Data as an ecosystem of 3Cs of Big Data: its crumbs, its capacities, and its communities. The chapter ends by questioning whether these kinds of digital data may replace traditional data and whether Big Data may render the scientific method obsolete, answering by the negative but arguing that social science research will dramatically evolve in contact with Big Data, while in turn shaping Big Data. The subsequent four empirical chapters focus on different applications of CDR analytics to social problems:Chapter 2 uses CDRs from Telefónica in London in conjunction with other socio-demographic data including police records to attempt to predict future crime hotspots, and presents a model with a predictive power of close to 70\%. This chapter offers an example of one of the major functions of Big Data introduced in Chapter 1, its predictive function here understood as forecasting, alongside its descriptive, prescriptive, and discursive functions. It also provides an opportunity to discuss some key tools and concepts commonly used in machine-learning as well as merits and limits of these approaches to crime prediction for public policy. Chapter 3 uses CDRs from Orange in Côte d’Ivoire made available as part of the 2013 Data for Development challenge---a modality that has been the hallmark of the field and contributed to developing Big Data communities over time---alongside meteorological data, with the goal of estimating whether weather could impact human mobility in ways that may violate the exclusion restriction in research using rainfalls as an instrument from economic conditions to assess the causal link between economic conditions and conflict. It presents a statistically significant relationship, suggesting that weather could affect conflict through other channels than economic conditions and casting doubt on the use of rainfalls as an instrument for economic conditions in these settings. Chapter 4 uses the same dataset and attempts to predict, here in the sense of inferring or now-casting, the multi-poverty index based on DHS data to assess whether and how these kinds of data available at high levels of temporal and geographical granularities may help some of the data gaps that characterize and may impede the development of some of the poorest countries in the world, showing promising results. Chapter 5 uses similar data as in Côte d'Ivoire but for Senegal, in conjunction with census data, to address the central issue of sample bias in big data by correlating estimated population size through cell-phone activity and census data. It proposes a novel approach to estimating biases in the data as a function of key demographic variables including age at different geographic levels. Chapter 6 finally focuses on political economy implications of Big Data as an ecosystem and socio-technological phenomenon, with a focus on its prospects and requirements, including institutional, legal, ethical, and political. It shows that Big Data in general and CDR analytics in particular raise complex and contentious questions for social science research, policymaking, and societies at large---including around power dynamics, informed consent, fairness, and civic participation etc., which will require significant investments in developing adequate responses, including to human awareness and capacities. It also argues, as does the overall conclusion, that Big Data and open algorithms notably can provide an entry and anchor point to challenge and improve the current state of the world by giving data emitters---citizens---greater control over the use of the data they generate in ways that could revive democratic ideals and principles and make it a potentially truly revolutionary force

The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good

The unprecedented availability of large-scale human behavioraldata is profoundly changing the world we live in. Researchers, companies,governments, financial institutions, non-governmental organizations and alsocitizen groups are actively experimenting, innovating and adapting algorith-mic decision-making tools to understand global patterns of human behaviorand provide decision support to tackle problems of societal importance. In thischapter, we focus our attention on social good decision-making algorithms,that is algorithms strongly influencing decision-making and resource opti-mization of public goods, such as public health, safety, access to finance andfair employment. Through an analysis of specific use cases and approaches,we highlight both the positive opportunities that are created through data-driven algorithmic decision-making, and the potential negative consequencesthat practitioners should be aware of and address in order to truly realizethe potential of this emergent field. We elaborate on the need for these algo-rithms to provide transparency and accountability, preserve privacy and betested and evaluated in context, by means of living lab approaches involvingcitizens. Finally, we turn to the requirements which would make it possible toleverage the predictive power of data-driven human behavior analysis whileensuring transparency, accountability, and civic participation