Annual Review of Economics - Volume 7, 2015

In this review, we argue that the Schumpeterian growth paradigm, which models growth as resulting from innovations involving creative destruction, sheds light on several aspects of the growth process that cannot be properly addressed by alternative theories. We focus on three important aspects for which Schumpeterian growth theory delivers predictions that distinguish it from other growth models, namely, (a) the role of competition and market structure, (b) firm dynamics, and (c) the relationship between growth and development.

We review the emerging literature on climate and conflict. We consider multiple types of human conflict, including both interpersonal conflict, such as assault and murder, and intergroup conflict, including riots and civil war. We discuss key methodological issues in estimating causal relationships and largely focus on natural experiments that exploit variation in climate over time. Using a hierarchical meta-analysis that allows us to both estimate the mean effect and quantify the degree of variability across 55 studies, we find that deviations from moderate temperatures and precipitation patterns systematically increase conflict risk. Contemporaneous temperature has the largest average impact, with each 1σ increase in temperature increasing interpersonal conflict by 2.4% and intergroup conflict by 11.3%. We conclude by highlighting research priorities, including a better understanding of the mechanisms linking climate to conflict, societies’ ability to adapt to climatic changes, and the likely impacts of future global warming.

How large are the gains from product market integration—or, equivalently, from a reduction in barriers to trade over space? This article surveys recent work on this question in the context of both international and intranational trade.

We present an expository, general analysis of valid post-selection or post-regularization inference about a low-dimensional target parameter in the presence of a very high-dimensional nuisance parameter that is estimated using selection or regularization methods. Our analysis provides a set of high-level conditions under which inference for the low-dimensional parameter based on testing or point estimation methods will be regular despite selection or regularization biases occurring in the estimation of the high-dimensional nuisance parameter. A key element is the use of so-called immunized or orthogonal estimating equations that are locally insensitive to small mistakes in the estimation of the high-dimensional nuisance parameter. As an illustration, we analyze affine-quadratic models and specialize these results to a linear instrumental variables model with many regressors and many instruments. We conclude with a review of other developments in post-selection inference and note that many can be viewed as special cases of the general encompassing framework of orthogonal estimating equations provided in this article.