External and internal determinants of development

As Rodrik, Subramanian, and Trebbi (2004) point out, factors that affect economic development can be classified using a two-tier approach. Based on a standard production function, inputs such as labor and physical and human capital directly affect per capita income. Much of the empirical cross-country growth literature has focused on these covariates. But the factors themselves are the product of deeper and more fundamental determinants and, thus, are at best proximate factors of economic development. The deeper determinants fall into two broad categories: internal and external. Among the former, institutions and geography have received the most attention, while international trade has been the focus of the latter. The main purpose of this paper is to add an external factor, namely measures of migration, to the existing geography-institutions-trade setup and to evaluate its contribution to the observed differences in per capita income across countries.Emigration and immigration ; International trade ; Economic development ; Developing countries ; Geography

Politics and Trade Policy: An Empirical Investigation

In this paper we examine the empirical relevance of three prominent endogenous protection models. Is protection for sale, or do altruistic policy makers worry about political support? We find strong evidence that protection is indeed "for sale." The important new result is, however, that not only the existence of lobbies matters, but also the relative size of the sectoral pro and anti protection contributions. All variables of both the Influence Driven (Grossman and Helpman, 1994) and the Tariff Function (Findlay and Wellisz, 1982) models are significant at the one percent level. Novel is our application of a single, unified theoretical framework to take strict interpretations of the three theoretical models to the data. We thus extend the previous tests of the Influence Driven approach by comparing its performance to well specified alternatives. Using J tests to compare the power of the models directly, we find significant misspecification in the Political Support Function approach. We cannot reject the null hypothesis of correct specification of the Influence Driven model and find evidence of some misspecification in the Tariff Function model.Endogenous protection, lobbying, political economy of tariffs

Trade, Wages, and Specific Factors

In this paper, we use a multi-sector specific factors model with sector-specific capital and two mobile factors, production and non-production labor, to examine the effects of globalization on the skill premium in U.S.\ manufacturing industries. A key feature of this model is that factor-price insensitivity does not hold, and thus endowment changes and factor-specific technological change affect relative factor returns, and hence the skill premium. Using this model and data for the U.S. manufacturing sector from 1958-94, we calculate changes in the skill premium and then carry out a decomposition to identify the changes caused by globalization, technological progress, and endowment changes. We find the model to be an accurate predictor of both the direction and magnitude of changes in the skill premium. The decomposition reveals that globalization effects, working through product price changes, were small in magnitude and caused the skill premium to decline during the '70s and '80s. In contrast, changes in capital endowments had a strong positive effect on the skill premium throughout the entire sample period. Sector specific and production labor specific technological change also had a positive impact on the premium, while non-production labor specific technological change increased the premium in all decades. Finally, changes in labor endowments caused a decline in the premium during all three decades.

Migration, trade, and development: an overview

Simple, neoclassical economic models predict that prices should drive factors such as labor and capital across regions and countries toward their most valuable use. As this happens, developing countries, which are typically labor-rich and capital-scarce, should experience more rapid growth, higher income, and eventually convergence to industrial world levels of well-being. This process is happening slowly in some cases, but in other cases not at all. Do migration and trade speed this convergence? If so, how? If not, why?Emigration and immigration ; International trade ; Economic development ; Developing countries

Did China Tire Safeguard Save U.S. Workers?

It has been well documented that trade adjustment costs to workers due to globalization are significant and that temporary trade barriers have been progressively used in many countries, especially during periods with high unemployment rates. Consequently, temporary trade barriers are perceived as a feasible policy instrument for securing domestic jobs in the presence of increased globalization and economic downturns. However, no study has assessed whether such temporary barriers have actually saved domestic jobs. To overcome this deficiency, we evaluate the China-specific safeguard case on consumer tires petitioned by the United States. Contrary to claims made by the Obama administration, we find that total employment and average wages in the tire industry were unaffected by the safeguard using the ‘synthetic control’ approach proposed by Abadie et al. (2010). Further analysis reveals that this result is not surprising as we find that imports from China are completely diverted to other exporting countries partly due to the strong presence of multinational corporations in the world tire market

Recognizing multimodal entailment

How information is created, shared and consumed has changed rapidly in recent decades, in part thanks to new social platforms and technologies on the web. With ever-larger amounts of unstructured and limited labels, organizing and reconciling information from different sources and modalities is a central challenge in machine learning. This cutting-edge tutorial aims to introduce the multimodal entailment task, which can be useful for detecting semantic alignments when a single modality alone does not suffice for a whole content understanding. Starting with a brief overview of natural language processing, computer vision, structured data and neural graph learning, we lay the foundations for the multimodal sections to follow. We then discuss recent multimodal learning literature covering visual, audio and language streams, and explore case studies focusing on tasks which require fine-grained understanding of visual and linguistic semantics question answering, veracity and hatred classification. Finally, we introduce a new dataset for recognizing multimodal entailment, exploring it in a hands-on collaborative section. Overall, this tutorial gives an overview of multimodal learning, introduces a multimodal entailment dataset, and encourages future research in the topic