International trade economists are used to controlling for common languages in most studies on the determinants of bilateral trade. The usual measure of common language is a binary variable based on one or several common official languages. The use of this measure lies mainly in the difficulty of quantifying common language use more thoroughly. However, it is not obvious that a common official language adequately reflects the broader impact of language commonality on trade, including language-related ethnic ties and trust and the mere ability to communicate. For this reason, the impact of a binary common official language variable on bilateral trade might mismeasure the role of common languages on bilateral trade at large. In a recent study, Melitz and Toubal (2014) provide an important step toward the understanding of the impact of common languages on bilateral trade based on data on 42 common native and spoken languages in 195 countries. They find that the joint impact of different aspects of common languages is at least twice as large as the one of a common official language. Their findings, moreover, suggest that common spoken languages are particularly important, and the ease of communication plays a substantial role in explaining the role of common languages for bilateral trade.

We propose an exact decomposition of sectoral annual sales growth in 1995-2020 through the lens of global input-output analysis across 26 sectors and 189 countries. We document that, apart from the growth of demand, changes in Leontief-inverse coefficients were a key driver for total sales growth, much more so than the geography of final goods and services trade. And we find that labour-market, property-rights or tax policies impact all the components of sales growth.

Countries trade more if they liberalized their trade relationship earlier. We derive a gravity equation featuring this path dependence due to sunk market-access costs that generate incumbency effects. We provide supporting evidence for the underlying mechanism and derive an augmented ACR (Arkolakis, Costinot, and Rodr & iacute;guez-Clare 2012) formula for the gains from trade that accounts for incumbency effects. A quantification suggests our mechanism explains up to 25% of countries' home shares, and the gains from trade are, on average, 10% larger when allowing for incumbency effects. The analysis further reveals novel distributional effects of trade, boosting real wages but reducing profits.

A large body of theoretical and quantitative work concerns models of heterogeneous firms and monopolistic competition. But most of it relies on strong assumptions regarding demand structure, firm-productivity distribution, and country heterogeneity. This paper studies a general-equilibrium model with directly explicitly additive preferences, non-specified productivity distributions, and asymmetric countries, for which much less is known. We first prove the existence and uniqueness of the market equilibrium with a three-stage approach of analyzing competition intensities and wages. We then explore the market-allocation mechanism and provide a baseline comparison between the market and a utilitarian optimum from a global planner's perspective. We show that misallocation in open economies can be decomposed into two effects, driven by country asymmetry and the variable elasticity of substitution. We present two examples exhibiting constant and variable markups, respectively, to illustrate how to apply our general theorem.

Time-series information on building stock is of paramount importance to study cities in a host of disciplines ranging from economics to urban planning. Such data are lacking in a consistently measured way and especially among dynamically growing cities in developing countries. Due to their rapid change, building stock data in these cities can offer insights into the determinants and consequences of urbanization. To be able to analyze urban structures effectively, the building stock needs to be measured with sufficient detail-at a resolution that makes individual buildings or small conglomerates thereof visible-and it needs to consider building height (or volume) with a satisfactory scope across cities to cover both large numbers and multi-year sequences of data. This study aims to develop a comprehensive pipeline for predicting building volume-including both footprint and height-across 1,537 urban areas in mainland China, covering more than 60% of the Chinese population over a seven-year period (2017-2023). With the advancement of deep learning in remote sensing, we can leverage stateof-the-art techniques to efficiently produce large-scale data for Chinese cities across years, which could be very time-consuming with traditional remote-sensing techniques. We compare the performance of several deep learning architectures for the task at hand. We demonstrate that the best performing approach leads to credible metrics of both footprint and height predictions and performs very competitively with respect to existing building-volume predictions. We also benchmark our results against other data sources such as real-estate listings and demonstrate the out-of-sample prediction capability of the proposed model.

The use of high-dimensional fixed-effects estimation has become customary with the estimation of gravity models of bilateral trade, migration, or commuting as outcome. However, fixed-effects methods can be used without incidental-parameter bias in a very small set of stochastic models. Alternatives to fixed-effects estimation are iterative-structural model estimation or linearizations of the structural model. Baier and Bergstrand deployed such a linearization. While easy to implement, the approach has drawbacks related to the approximation point and lack of observability of ingredients needed for the linearization. This compromises empirical work. The present paper provides a remedy to this problem by linearizing at the observed trade equilibrium.

We assess theoretical predictions regarding the determinants of domestic intra-industry trade in large panel data of pairs of 276 cities, as well as 42 sectors, over three years in China. We find that geographical and technological distances are the main impediments of intra-industry trade, being more important than relative endowment differences. This confirms expectations based on new trade theory in a novel data environment.

Over the last few decades, multi-indexed data on trade, multinational activity, and even migration have become available. By far the most prominent application of multi-dimensional data in the context of international economics is the estimation of the famous gravity equation of international trade, where bilateral export or import volume (or foreign direct investment stock or migration stock) is the dependent variable of interest. This chapter provides a survey of empirical issues in gravity-model estimation from a panel econometric perspective. It sets off with a generic illustration of the theoretical foundations of gravity equations and proceeds with the modelling of the multi-dimensional stochastic structure, focusing on fixed-effects estimation.

Recycling waste from used goods can substitute for scarce virgin materials and reduce resource dependence. We present a model of waste collection, recycling, and final goods production using virgin and recycled materials. Environmentally safe disposal of trash (non-recycled waste) requires costly processing by landfill and burning which creates externalities. Trade between resource poor and resource rich countries involves non-trivial interactions between terms of trade effects and distortions in recycling and resource extraction. We analyze welfare improving policy intervention with local trash disposal and with trade in trash.

This article combines insights from three strands of work: the one on self-selection into treatments, the one on staggered treatment effects, and the one on preferential trade agreements (PTAs). We illustrate in a staggered treatment effects approach with selection on unobservables that in the phase since 1997, when exogenous staggered PTA effects had been found of insignificant importance, endogenous staggered PTAs exert positive effects that are statistically significant.

This paper proposes an imputation of a global input-output matrix, where China is broken up into 332 prefecture-type regions in three years of data, 2007, 2012, and 2017. Using the resulting global input-output matrix, the paper documents that sizable spillover effects exist with regard to economic volatility. In particular, such volatility spillovers are important for prices and somewhat less so for quantity shocks. We demonstrate that individual Chinese prefectures are large recipients and donors of such shocks. All Chinese prefectures together have a very large impact on the world economy in terms of the considered volatility shocks.

This paper focuses on the effect of preferential trade agreements and their depth on firm-to-firm ownership, in particular, along global value chains. It measures shareholder-affiliate ownership links at the country-sector-pair level to distinguish between vertical and horizontal links. The findings show that preferential trade agreements boost vertical international investment links (both backward and forward) while reducing horizontal investment. Deep preferential trade agreements stimulate investment particularly for sector pairs, where a high input specificity prevails.

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This paper sets up a two-country model of offshoring with monopolistically competitive product and monopsonistically competitive labor markets. In our model, an incentive for offshoring exists even between symmetric countries, because shifting part of the production abroad reduces local labor demand and allows firms to more strongly execute their monopsonistic labor market power. However, offshoring between symmetric countries has negative welfare effects and therefore calls for policy intervention. In this context, we put forward the role of a common minimum wage and show that the introduction of a moderate minimum wage increases offshoring and reduces welfare. In contrast, a sizable minimum wage reduces offshoring and increases welfare. Beyond that, we also show that a sufficiently high common minimum wage cannot only eliminate offshoring but also inefficiencies in the resource allocation due to monopsonistic labor market distortions in closed economies.

Inspired by the increased interest in economic sanctions and their consequences, this special issue contains a collection of studies by experts aiming to reflect the recent developments and trends in the literature on economic sanctions. The contributions contain theoretical research on the topic, data collection, and empirical work on the impact, effectiveness and success of sanctions. Moreover, the contributions come from economists and political scientists and are, therefore, interdisciplinary in nature. In this introduction, we highlight each paper in the volume by summarizing its salient features and by placing them in the broader context of the literature on economic sanctions. We also synthesize several takeaways and conclude by identifying questions we believe future research should shed further light on.

We explore how the type of global market entry affects wage premia, classifying firms into four categories: domestic only, domestic exporters, non-exporting multinationals, and exporting multinational enterprises. Using firm-level panel data for Bosnia and Herzegovina, Croatia, and Slovenia for the years 2007–2017 and a multivariate endogenous treatment model based on the approach of Wooldridge (J Econom 68(1):115–132, 1995 ), we find that the multinational wage premia are mainly driven by the export status of multinational firms. Specifically, domestic exporters and exporting multinationals pay on average higher wages than non-exporting firms, whereas non-exporting multinationals tend to pay lower wages than domestic-only firms.

This is the introduction to the Special Issue on "China and the global economy". It provides a brief overview of the articles it contains.