Services Liberalization and Export Diversity: Theory and Evidence from Chinese Firms

During the last decades, we observe a liberalization trend in the services sector globally. Using the Chinese exporting firm data, this paper studies how multi-product firms adjust their export strategies in response to the services trade liberalization across export destination countries. Our study finds a highly significant positive relation between the services trade liberalization in the destination countries and each firm's export diversify, which is measured as the product scope, the Herfindahl-Hirschman style index, or the value skewness across varieties,export product switch. Our empirical analysis further finds that firms increase the relatedness of their exporting varieties towards the OECD countries, but reduce it towards the non-OECD countries. With a conventional multi-product firm model, we explore the mechanisms behind all our empirical findings

Services Liberalization and Export Diversity: Theory and Evidence from Chinese Firms

During the last decades, we observe a liberalization trend in the services sector globally. Using the Chinese exporting firm data, this paper studies how multi-product firms adjust their export strategies in response to the services trade liberalization across export destination countries. Our study finds a highly significant positive relation between the services trade liberalization in the destination countries and each firm's export diversify, which is measured as the product scope, the Herfindahl-Hirschman style index, or the value skewness across varieties,export product switch. Our empirical analysis further finds that firms increase the relatedness of their exporting varieties towards the OECD countries, but reduce it towards the non-OECD countries. With a conventional multi-product firm model, we explore the mechanisms behind all our empirical findings

Services Liberalization and Export Diversity: Theory and Evidence from Chinese Firms

During the last decades, we observe a liberalization trend in the services sector globally. Using the Chinese exporting firm data, this paper studies how multi-product firms adjust their export strategies in response to the services trade liberalization across export destination countries. Our study finds a highly significant positive relation between the services trade liberalization in the destination countries and each firm's export diversify, which is measured as the product scope, the Herfindahl-Hirschman style index, or the value skewness across varieties,export product switch. Our empirical analysis further finds that firms increase the relatedness of their exporting varieties towards the OECD countries, but reduce it towards the non-OECD countries. With a conventional multi-product firm model, we explore the mechanisms behind all our empirical findings

One Objective to Rule Them All: A Maximization Objective Fusing Estimation and Planning for Exploration

In online reinforcement learning (online RL), balancing exploration and exploitation is crucial for finding an optimal policy in a sample-efficient way. To achieve this, existing sample-efficient online RL algorithms typically consist of three components: estimation, planning, and exploration. However, in order to cope with general function approximators, most of them involve impractical algorithmic components to incentivize exploration, such as optimization within data-dependent level-sets or complicated sampling procedures. To address this challenge, we propose an easy-to-implement RL framework called \textit{Maximize to Explore} (\texttt{MEX}), which only needs to optimize \emph{unconstrainedly} a single objective that integrates the estimation and planning components while balancing exploration and exploitation automatically. Theoretically, we prove that \texttt{MEX} achieves a sublinear regret with general function approximations for Markov decision processes (MDP) and is further extendable to two-player zero-sum Markov games (MG). Meanwhile, we adapt deep RL baselines to design practical versions of \texttt{MEX}, in both model-free and model-based manners, which can outperform baselines by a stable margin in various MuJoCo environments with sparse rewards. Compared with existing sample-efficient online RL algorithms with general function approximations, \texttt{MEX} achieves similar sample efficiency while enjoying a lower computational cost and is more compatible with modern deep RL methods