Published and Forthcoming Papers
A. Advani, T. Kitagawa and T. Słoczyński (2019), Journal of Applied Econometrics
We consider two recent suggestions for how to perform an empirically motivated Monte Carlo study to help select a treatment effect estimator under unconfoundedness. We show theoretically that neither is likely to be informative except under restrictive conditions that are unlikely to be satisfied in many contexts. To test empirical relevance, we also apply the approaches to a real-world setting where estimator performance is known. Both approaches are worse than random at selecting estimators which minimise absolute bias. They are better when selecting estimators that minimise mean squared error. However, using a simple bootstrap is at least as good and often better. For now researchers would be best advised to use a range of estimators and compare estimates for robustness.
A. Advani and B. Malde (2018), Journal of Economic Surveys
Understanding whether and how connections between agents (networks) such as declared friendships in classrooms, transactions between firms, and extended family connections, influence their socio-economic outcomes has been a growing area of research within economics. Early methods developed to identify these social effects assumed that networks had formed exogenously, and were perfectly observed, both of which are unlikely to hold in practice. A more recent literature, both within economics and in other disciplines, develops methods that relax these assumptions. This paper reviews that literature. It starts by providing a general econometric framework for linear models of social effects, and illustrates how network endogeneity and missing data on the network complicate identification of social effects. Thereafter, it discusses methods for overcoming the problems caused by endogenous formation of networks. Finally, it outlines the stark consequences of missing data on measures of the network, and regression parameters, before describing potential solutions.
A. Advani and B. Malde (2018), Swiss Journal of Economics and Statistics (solicited)
In many contexts we may be interested in understanding whether direct connections between agents, such as declared friendships in a classroom or family links in a rural village, affect their outcomes. In this paper we review the literature studying econometric methods for the analysis of linear models of social effects, a class that includes the `linear-in-means' local average model, the local aggregate model, and models where network statistics affect outcomes. We provide an overview of the underlying theoretical models, before discussing conditions for identification using observational and experimental/quasi-experimental data.
A. Advani and B. Malde (2014), IFS Working Paper W14/34
In many contexts we may be interested in understanding whether direct connections between agents, such as declared friendships in a classroom or family links in a rural village, affect their outcomes. In this paper we review the literature studying econometric methods for the analyis of social networks. We begin by providing a common framework for models of social effects, a class that includes the ‘linear-in-means’ local average model, the local aggregate model, and models where network statistics affect outcomes. We discuss identification of these models using both observational and experimental/quasi-experimental data. We then discuss models of network formation, drawing on a range of literatures to cover purely predictive models, reduced form models, and structural models, including those with a strategic element. Finally we discuss how one might collect data on networks, and the measurement error issues caused by sampling of networks, as well as measurement error more broadly.