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Internet data hold many promises for demographic research, but come with severe drawbacks due to several types of bias. The purpose of this paper is to review the literature that uses internet data for demographic studies and presents a general framework for addressing the problem of selection bias in non-representative samples.

The authors propose two main approaches to reduce bias. When ground truth data are available, the authors suggest a method that relies on calibration of the online data against reliable official statistics. When no ground truth data are available, the authors propose a difference in differences approach to evaluate relative trends.

The authors offer a generalization of existing techniques. Although there is not a definite answer to the question of whether statistical inference can be made from non-representative samples, the authors show that, when certain assumptions are met, the authors can extract signal from noisy and biased data.

The methods are sensitive to a number of assumptions. These include some regularities in the way the bias changes across different locations, different demographic groups and between time steps. The assumptions that we discuss might not always hold. In particular, the scenario where bias varies in an unpredictable manner and, at the same time, there is no “ground truth” available to continuously calibrate the model, remains challenging and beyond the scope of this paper.

The paper combines a critical review of existing substantive and methodological literature with a generalization of prior techniques. It intends to provide a fresh perspective on the issue and to stimulate the methodological discussion among social scientists.

The purpose of this paper is to recommend the use of internet data for social sciences with a special focus on human resources issues. It discusses the potentials and challenges of internet data for social sciences. The authors present a selection of the relevant literature to establish the wide spectrum of topics, which can be reached with this type of data, and link them to the papers in this International Journal of Manpower special issue.

Internet data are increasingly representing a large part of everyday life, which cannot be measured otherwise. The information is timely, perhaps even daily following the factual process. It typically involves large numbers of observations and allows for flexible conceptual forms and experimental settings.

Internet data can successfully be applied to a very wide range of human resource issues including forecasting (e.g. of unemployment, consumption goods, tourism, festival winners and the like), nowcasting (obtaining relevant information much earlier than through traditional data collection techniques), detecting health issues and well-being (e.g. flu, malaise and ill-being during economic crises), documenting the matching process in various parts of individual life (e.g. jobs, partnership, shopping), and measuring complex processes where traditional data have known deficits (e.g. international migration, collective bargaining agreements in developing countries). Major problems in data analysis are still unsolved and more research on data reliability is needed.

The data in the reviewed literature are unexplored and underused and the methods available are confronted with known and new challenges. Current research is highly original but also exploratory and premature.

The paper reviews the current attempts in the literature to incorporate internet data into the mainstream of scholarly empirical research and guides the reader through this Special Issue. The authors provide some insights and a brief overview of the current state of research.