This paper takes the view that culture and identity play an important role in our understanding of migration as an economic phenomenon. In addition, what is often omitted from many economic studies is the fact that culture and identity are endogenous and influenced by migration. I study preference evolution across two different regions based on endogenous cultural transmission at a regional level. Voluntary and involuntary migrations across regions are allowed, as are differences in economic advancement of the two regions. I characterize various steady state equilibria with corresponding existence conditions, and assess their welfare properties. ∗This paper is written towards satisfying the mandatory economic history requirement as a part of the PhD program. I am grateful to Avner Greif for comments. †Department of Economics, Stanford University, 579 Serra Mall, Stanford, CA, 94305. Email: adugalic@stanford.edu.

We study how short-sale constraints on stock lending affect asset prices in an equilibrium model with multiple assets. We endow investors with heterogeneous beliefs in order to generate short selling demand. We obtain a CAPM-like equation that links asset-specific excess returns with the market equity premium. In the presence of short selling constraints in the market, the model gives rise to assetspecific alphas that are explained by both asset-specific and market-wide short-sale constraints; unconstrained stocks have higher risk-adjusted expected returns relative to the market portfolio, whereas the opposite holds for constrained stocks. In the absence of short-sale constraints, the model reduces to the standard CAPM. We test the model using extensive data on short interest and borrow fees. The model is able to empirically explain asset prices for 10 borrow-fee-sorted portfolios, as opposed to CAPM and factor models which produce unexplained alphas that are significantly different from zero for some low and high borrow fee portfolios. ∗We are extremely grateful to Jonathan Berk, Shai Bernstein, Nick Bloom, Svetlana Bryzgalova, and Isaac Sorkin for helpful conversations and suggestions, as well as to Kevin Mak for practical insights on the institutional details of the short selling market. All errors are ours. †Department of Economics, Stanford University, 579 Serra Mall, Stanford, CA 94305. Email: adugalic@stanford.edu. Website: http://www.stanford.edu/∼adugalic. ‡Department of Economics, Stanford University, 579 Serra Mall, Stanford, CA 94305. Email: dtorresp@stanford.edu.

We propose several algorithms to reduce errors of classification created by using imperfect training sets. A classic example is misallocation of scarce funds to poor households due to unobserved income in combination with data misreporting or corruption at the administrative level. Suppose there are several training examples in which the targets are misclassified at a certain rate. Since the errors are imperfectly correlated across training examples, wrongly classified observations in one sample will share common characteristics with observations that are differently classified in other samples. We use this insight to develop several machine learning algorithms to reduce this classification error. We apply the algorithms to the context of aid programs targeting poor households with limited knowledge of their true income and poverty ranking. In our mainline specification of the problem, our main algorithm makes a 37% improvement in the allocation of the funds. ∗This paper is an extended version of a project developed in a machine learning course at Stanford. †Department of Economics, Stanford University, 579 Serra Mall, Stanford, CA 94305, adugalic@stanford.edu. ‡Department of Economics, Stanford University, 579 Serra Mall, Stanford, CA 94305, tram@stanford.edu 1