The Augmented Synthetic Control Method

The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit in panel data settings. The "synthetic control" is a weighted average of control units that balances the treated unit's pre-treatment outcomes as closely as possible. A critical feature of the original proposal is to use SCM only when the fit on pre-treatment outcomes is excellent. We propose Augmented SCM as an extension of SCM to settings where such pre-treatment fit is infeasible. Analogous to bias correction for inexact matching, Augmented SCM uses an outcome model to estimate the bias due to imperfect pre-treatment fit and then de-biases the original SCM estimate. Our main proposal, which uses ridge regression as the outcome model, directly controls pre-treatment fit while minimizing extrapolation from the convex hull. This estimator can also be expressed as a solution to a modified synthetic controls problem that allows negative weights on some donor units. We bound the estimation error of this approach under different data generating processes, including a linear factor model, and show how regularization helps to avoid over-fitting to noise. We demonstrate gains from Augmented SCM with extensive simulation studies and apply this framework to estimate the impact of the 2012 Kansas tax cuts on economic growth. We implement the proposed method in the new augsynth R package

A review of domain adaptation without target labels

Domain adaptation has become a prominent problem setting in machine learning and related fields. This review asks the question: how can a classifier learn from a source domain and generalize to a target domain? We present a categorization of approaches, divided into, what we refer to as, sample-based, feature-based and inference-based methods. Sample-based methods focus on weighting individual observations during training based on their importance to the target domain. Feature-based methods revolve around on mapping, projecting and representing features such that a source classifier performs well on the target domain and inference-based methods incorporate adaptation into the parameter estimation procedure, for instance through constraints on the optimization procedure. Additionally, we review a number of conditions that allow for formulating bounds on the cross-domain generalization error. Our categorization highlights recurring ideas and raises questions important to further research.Comment: 20 pages, 5 figure

Trade, Standards and Poverty. Evidence from Senegal

The debate on trade and poverty is reinforced by recent studies on the role of standards. It is argued that increasing standards act as trade barriers for developing countries and cause further marginalization of the poor. This paper is the first to quantify income and poverty effects of such high-standards trade and to integrate labor market effects, by using company and household survey data from the vegetable export chain in Senegal. We find that exports have grown sharply despite increasing standards, resulting in important income gains and poverty reduction. Our estimates indicate that poverty is 14 % points lower due to vegetable exports. Tightening food standards induced a shift from smallholder contract-based farming to large-scale integrated estate production, altering the mechanism through which poor households benefit: through labor markets instead of product markets. The impact on poverty reduction is stronger as the poorest benefit relatively more from working on large-scale farms than from contract farming.trade, poverty, standards, vertical coordination, contract farming, Food Security and Poverty, International Relations/Trade, F14, F16, I3, Q12, Q13, Q17,

Trade, standards and poverty: Evidence from Senegal.

Trade; Exports;

Instrumental variables quantile regression for panel data with measurement errors

This paper develops an instrumental variables estimator for quantile regression in panel data with fixed effects. Asymptotic properties of the instrumental variables estimator are studied for large N and T when Na/T ! 0, for some a > 0. Wald and Kolmogorov-Smirnov type tests for general linear restrictions are developed. The estimator is applied to the problem of measurement errors in variables, which induces endogeneity and as a result bias in the model. We derive an approximation to the bias in the quantile regression fixed effects estimator in the presence of measurement error and show its connection to similar effects in standard least squares models. Monte Carlo simulations are conducted to evaluate the finite sample properties of the estimator in terms of bias and root mean squared error. Finally, the methods are applied to a model of firm investment. The results show interesting heterogeneity in the Tobin’s q and cash flow sensitivities of investment. In both cases, the sensitivities are monotonically increasing along the quantiles