We examine long-term neighborhood effects on low-income families using data from the Moving to Opportunity (MTO) randomized housing-mobility experiment, which offered some public-housing families but not others the chance to move to less-disadvantaged neighborhoods. We show that 10-15 years after baseline, MTO improves adult physical and mental health, has no detectable effect on economic outcomes or youth schooling or physical health, and mixed results by gender on other youth outcomes, with girls doing better on some measures and boys doing worse. Despite the somewhat mixed pattern of impacts on traditional behavioral outcomes, MTO moves substantially improve adult subjective well-being.
We build a theoretical model of multi-product firms that highlights how competition across market destinations affects both a firm's exported product range and product mix. We show how tougher competition in an export market induces a firm to skew its export sales towards its best performing products. We find very strong confirmation of this competitive effect for French exporters across export market destinations. Theoretically, this within firm change in product mix driven by the trading environment has important repercussions on firm productivity. A calibrated fit to our theoretical model reveals that these productivity effects are potentially quite large.
We develop a matched signal detection (MSD) theory for signals with an intrinsic structure described by a weighted graph. Hypothesis tests are formulated under different signal models. In the simplest scenario, we assume that the signal is deterministic with noise in a subspace spanned by a subset of eigenvectors of the graph Laplacian. The conventional matched subspace detection can be easily extended to this case. Furthermore, we study signals with certain level of smoothness. The test turns out to be a weighted energy detector, when the noise variance is negligible. More generally, we presume that the signal follows a prior distribution, which could be learnt from training data. The test statistic is then the difference of signal variations on associated graph structures, if an Ising model is adopted. Effectiveness of the MSD on graph is evaluated both by simulation and real data. We apply it to the network classification problem of Alzheimer’s disease (AD) particularly. The preliminary results demonstrate that our approach is able to exploit the sub-manifold structure of the data, and therefore achieve a better performance than the traditional principle component analysis (PCA).
