Using big data in official statistics: Why? When? How? What for?

[EN] This paper analyses the potential usefulness of big data in official statistics starting from four key questions such as Why? When? How? and What for - should we use big data in official statistics? To derive some answers related to empirical cases. This paper presents a big data classification by types, which is then used to identify how big data can answer to specific information needs in key policy areas. Based on the findings of these investigations, some very provisional and subjective answers to the questions raised above are derived.Mazzi, GL. (2018). Using big data in official statistics: Why? When? How? What for?. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 237-245. https://doi.org/10.4995/CARMA2018.2018.8576OCS23724

Manipulation-Proof Machine Learning

An increasing number of decisions are guided by machine learning algorithms. In many settings, from consumer credit to criminal justice, those decisions are made by applying an estimator to data on an individual's observed behavior. But when consequential decisions are encoded in rules, individuals may strategically alter their behavior to achieve desired outcomes. This paper develops a new class of estimator that is stable under manipulation, even when the decision rule is fully transparent. We explicitly model the costs of manipulating different behaviors, and identify decision rules that are stable in equilibrium. Through a large field experiment in Kenya, we show that decision rules estimated with our strategy-robust method outperform those based on standard supervised learning approaches

Can call detail records provide insights into women’s empowerment? A case study from Uganda

We use CDRs of mobile phone users in Uganda combined with data from a phone survey to train machine-learning models to predict the sex of the mobile phone user and several indicators of economic empowerment such as ownership of a house and land, occupation, and decision-making over household income. The most accurate of the models predicts the sex of the mobile phone user with 78% accuracy. The different indicators of economic empowerment are predicted with accuracies ranging from 57% to 61%. We also predict users’ sex and economic empowerment jointly. However, when we predict economic empowerment and then the sex of the user, we achieve high accuracy rates ranging from 81% to 87%. Mobile phone usage data hold potential for gender research although they are not without limitations