Related papers: Estimating individual employment status using mobi…
Two distinct trends can prove the existence of technological unemployment in the US. First, there are more open jobs than the number of unemployed persons looking for a job, and second, the shift of the Beveridge curve. There have been many…
Can mobile phone data improve program targeting? By combining rich survey data from a "big push" anti-poverty program in Afghanistan with detailed mobile phone logs from program beneficiaries, we study the extent to which machine learning…
An intriguing open question is whether measurements made on Big Data recording human activities can yield us high-fidelity proxies of socio-economic development and well-being. Can we monitor and predict the socio-economic development of a…
Mobile phone usage provides a wealth of information, which can be used to better understand the demographic structure of a population. In this paper, we focus on the population of Mexican mobile phone users. We first present an…
This study provides evidence that personality can be reliably predicted from activity data collected through mobile phone sensors. Employing a set of well informed indicators calculable from accelerometer records and movement patterns, we…
Skill shortages are a drain on society. They hamper economic opportunities for individuals, slow growth for firms, and impede labor productivity in aggregate. Therefore, the ability to understand and predict skill shortages in advance is…
High quality census data are not always available in developing countries. Instead, mobile phone data are becoming a trending proxy to evaluate population density, activity and social characteristics. They offer additional advantages for…
Recent studies have shown the value of mobile phone data to tackle problems related to economic development and humanitarian action. In this research, we assess the suitability of indicators derived from mobile phone data as a proxy for…
Using images containing information on wealth, this research investigates that pictures are capable of reliably predicting the economic prosperity of households. Without surveys on wealth-related information and human-made standard of…
In this paper we predict outgoing mobile phone calls using a machine learning approach. We analyze to which extent the activity of mobile phone users is predictable. The premise is that mobile phone users exhibit temporal regularity in…
Poverty prediction models are used to address missing data issues in a variety of contexts such as poverty profiling, targeting with proxy-means tests, cross-survey imputations such as poverty mapping, top and bottom incomes studies, or…
This paper develops a new data-driven approach to characterizing latent worker skill and job task heterogeneity by applying an empirical tool from network theory to large-scale Brazilian administrative data on worker--job matching. We…
Determining the poverty levels of various regions throughout the world is crucial in identifying interventions for poverty reduction initiatives and directing resources fairly. However, reliable data on global economic livelihoods is hard…
The growing instability of both global and domestic economic environments has increased the risk of financial distress at the household level. However, traditional econometric models often rely on delayed and aggregated data, limiting their…
The massive amounts of geolocation data collected from mobile phone records has sparked an ongoing effort to understand and predict the mobility patterns of human beings. In this work, we study the extent to which social phenomena are…
Approximately half of the global population does not have access to the internet, even though digital connectivity can reduce poverty by revolutionizing economic development opportunities. Due to a lack of data, Mobile Network Operators and…
The information collected by mobile phone operators can be considered as the most detailed information on human mobility across a large part of the population. The study of the dynamics of human mobility using the collected geolocations of…
We analyze a coupled anonymized dataset collecting the mobile phone communication and bank transactions history of a large number of individuals. After mapping the social structure and introducing indicators of socioeconomic status,…
Ending poverty in all its forms everywhere is the number one Sustainable Development Goal of the UN 2030 Agenda. To monitor the progress towards such an ambitious target, reliable, up-to-date and fine-grained measurements of socioeconomic…
In this paper we apply a time series based Vector Auto Regressive (VAR) approach to the problem of predicting unemployment insurance claims in different census regions of the United States. Unemployment insurance claims data, reported…