Arun Advani

Methods to Identify Linear Network Models: A Review

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.

Credibly identifying social effects: accounting for network formation and measurement error

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.

Cheaper, Greener and More Efficient: Rationalising UK Carbon Prices*

Current UK energy use policies, which primarily aim to reduce carbon emissions, provide abatement incentives that vary by user and fuel, creating inefficiency. Distributional concerns are often given as a justification for the lower carbon price faced by households, but there is little rationale for carbon prices associated with the use of gas to be lower than those for electricity. We consider reforms that raise carbon prices faced by households and reduce the variation in carbon prices across gas and electricity use, improving the efficiency of emissions reduction. We show that the revenue raised from these reforms can be recycled in a way that ameliorates some of the distributional concerns. Whilst such recycling is not able to protect all poorer households, existing policy also makes distributional trade-offs, but does so in an opaque and inefficient way.