Matching Models

This paper introduces τ-fairness as a compromise solution reconciling Pareto efficiency and equity in School Choice Problems. We show that, by considering a weak notion of equity that we refer to as λ-equity, it is possible to contribute positively to solve an open debate, originated by the efficiency-equity trade-off of the schooling problem. We also suggest a slight modification to some allocative procedures recently introduced in the United States to compute τ-fair allocations and provide support for this (possible) reformulation.

Our research investigates the magnitude of the effect of residential energy efficiency audit programs on later household electricity consumption. These programs are designed to increase awareness of household energy consumption with personalized feedback that will eventually lead to behavioral changes. In this type of survey, there is only a one-time interaction between households, which participate voluntarily, and the surveyors. The objective of this study is to determine whether and to what extent such surveys lead to behavioral changes. We argue that the perceived complexity of the survey feedback will determine whether the subsequent behavior is sustainable. Then we analyze how persistent the intervention is over time and whether the effects decay or intensify. However, the main evaluation problem involving these surveys is self-selection bias. To correct for this bias, we propose two non-parametric estimators by using a kernel-based propensity score matching approach. In the first method, we use “difference-in-differences” (DID) estimations. The second estimator is quantile DID, which produces estimates on distributions. The comparison group consists of households who were not yet participating in the survey but participated later. The evidence suggest that the customers who participated in the survey reduced their electricity consumption by 6.7%, compared with customers who had not yet participated in the survey. In addition, as the quantiles of the distribution increase, the effect of the program decreases.

We consider data where we have individuals affiliated with at most one organisational unit and where the interest is in modelling changes to these affiliations over time. This could be the case of people working for organisations or people living in neighbourhoods. We draw on dynamic models for social networks to propose an actor-oriented model for how these affiliations change over time. These models specifically take into account constraints of the system and allow for the system
to be observed at discrete time-points. Constraints stem from the fact that for example  not  everybody  can  have  the  same  job  or  live  in  the  same  neighbourhood, something which induces dependencies among the decisions marginally. The model encompasses two modelling components: a model for determining the termination of an affiliation; and a discrete-choice model for determining the new affiliation. For
estimation we employ a Bayesian data-augmentation algorithm, that augments the observed states with unobserved sequences of transitions. We apply the proposed methods to a dataset of house-moves in Stockholm and illustrate how we may infer the mechanisms that sustain and perpetuate segregation on the housing market.