Essays on Industrial Policy and Innovation in an Open Economy

Just like people, industries have a lifecycle. My dissertation explores how trade and industrial policy affect innovation and welfare across the industry lifecycle—often called the ``product cycle.'' Recently, new frontier technologies such as artificial intelligence, robotics, and green energy technologies have been rapidly emerging in what Klaus Schwab has labelled the `Fourth Industrial Revolution' (\citealp{schwab2017fourth}). This dissertation suggests that by taking into account the industry lifecycle, policy can have a more significant impact on the welfare of not only the implementing country but also its counterparts, compared to a time when new industries have become well-established and mature.In the first chapter, I develop a simple open economy model that incorporates productivity dynamics suggested by the industry lifecycle. In this lifecycle, productivity is low and does not grow significantly in the early stage, then after a radical innovation, it grows very fast for a while before tapering off. The model suggests important policy implications. First, considering industry lifecycle when designing industrial policy is important since the growth potential and degree of externality vary depending on the stage of the targeted industry's lifecycle. Second, policymakers need to take into account the difference in timing when policy costs and benefits occur. The model shows that industrial policy reduces instantaneous utility in the short run due to distortions created by the policy, but it can increase overall welfare by accelerating innovation in the targeted industry in the long run. Third, home industrial policy can increase foreign welfare through the terms-of-trade effect, meaning the foreign country can benefit from the lower home product price due to home innovation.In the second chapter, I present a general framework for analyzing the welfare effects of industrial policy when a country is hastening to catch up to the technological frontier, versus racing to create new technologies. The model in this chapter, which incorporates industry lifecycle theory into an open economy macroeconomic model by \cite{corsetti2007}, provides distinct welfare implications in two scenarios: \emph{catch-up} and \emph{frontier technology races}. In the former scenario, the targeted industry is nascent with high growth potential at home, but mature abroad. In contrast, in the latter scenario, both the home and foreign industries have high growth potential and are in competition with each other. For the home country, a production subsidy accelerates innovation in the targeted industry and thus can enhance welfare in both scenarios, despite a trade-off between short-term losses and long-term gains. For the foreign country, in the catch-up scenario, a home production subsidy unambiguously increases foreign welfare. Conversely, in the scenario of frontier technology races, it may induce a beggar-thy-neighbor effect by delaying innovation abroad. In such circumstances, the foreign country responds by implementing aggressive countervailing policies to mitigate the negative spillover effects. If both countries instead cooperatively support the industry, the welfare outcome is a Pareto improvement compared to the Nash equilibrium.In the third chapter, I explore the reasons why many countries support industries essential for transitioning to a green economy, despite the cost of converting to green energy and the opportunities for free-riding on other countries' carbon abatement. By incorporating the negative externalities from greenhouse gas emissions into the open-economy macroeconomic model developed in Chapter \ref{ch:2ndChapterLabel}, I analyze the welfare effects of industrial policies that subsidize production of capital goods (like solar panels or wind turbines) used to produce green energy. The model predicts that a production subsidy for the green capital goods industry is desirable for the home country, as it accelerates innovation in the industry and consequently green energy adoption. This acceleration at home delays innovation abroad, generating a beggar-thy-neighbor effect, despite the environmental benefits from home innovation. Thus, in a Nash equilibrium, both nations competitively raise production subsidies, improving welfare in both countries by reducing distortions created by the subsidy and greenhouse gas emissions. A cooperative equilibrium still yields a Pareto improvement, given the incomplete resolution of the free-riding problem in the Nash equilibrium. To quantitatively analyze the welfare and environmental effects of policies implemented by the US and the EU, I estimate the innovation timing elasticity, showing for the first time that the pace of innovation increases with the number of firms operating in an industry. The estimate is sufficiently high to shift the optimal national policy from free-riding to subsidizing green capital goods production in the quantitative analysis

Receiver synchronization for UWB TDOA localization

Pulsed ultra-wideband (UWB) radio uses extremely short pulses to transmit information. Such pulses provide very fine timing information, which has led to technological advances in high-precision localization. This thesis investigates UWB localization strategies with a focus on receiver synchronization algorithms for time-difference-of-arrival localization to achieve centimeter accuracies in a 3-dimensional space. The system that consists of a reference transmitter, several receivers each with a sampling module and a wireless local area network interface, and a computer to process the data and to run the localization algorithm is targeted for deployment in a dense-multipath environment -- a metal-enclosed space with substantial amount of metallic objects inside it. An algorithm based on the existing reference broadcast synchronization with a coarse synchronization stage and a fine synchronization stage is developed for this application. Coarse synchronization synchronizes the clocks of the independently running receivers to within nanoseconds or tens of nanoseconds, and is implemented in an FPGA. The sampled data by all receivers are transmitted to a computer and then processed by a fine synchronization algorithm. This proposed algorithm is simulated in Matlab and Simulink. Major factors that may cause a synchronization error such as propagation delay and path overlap are modeled and included in this simulation model. In addition, challenges due to non-idealities of a practice environment are examined by implementing this algorithm in a properly working hardware. In particular, different sampling rates of all receivers are found to be a major issue that must be resolved. Different clock speeds affect both the coarse and the fine synchronization accuracies. Therefore, this thesis proposes a method that uses a reference signal with a stable pulse repetition frequency to overcome this issue

Automation, Human Task Innovation, and Labor Share: Unveiling the Role of Elasticity of Substitution

This paper investigates the elements contributing to the decline in labor share, with a specific focus on the roles of `automation' and `innovation in human tasks.' We construct a general equilibrium model that separately incorporates both robot and non-robot capital to derive an econometric specification. Based on regression results, we estimate the elasticity of substitution between labor and non-robot capital to be less than one, while the elasticity of substitution between tasks is greater than, but close to, one. Together with these estimates, our regression results yield three major findings. First, we identify two distinct channels through which robots affect labor share: automation and the decrease in the price of robots. Both channels are found to negatively impact labor share. Our general equilibrium model predicts that the effect of declining robot prices will intensify as robots become more prevalent. Second, we are the first to empirically evaluate the impact of human task innovation on labor share by constructing a novel index for new human tasks. Our accounting analysis suggests that the positive influence of human task innovation outweighs the adverse effects of automation. Lastly, by utilizing estimates of the elasticity of substitution between labor and non-robot capital, as well as between tasks, we elucidate the mechanisms through which factor prices affect the labor share. Specifically, we find that both the negative effect of automation and the positive effect of human task innovation are amplified through the aggregated task price channel

Automation, Human Task Innovation, and Labor Share: Unveiling the Role of Elasticity of Substitution

This paper investigates the elements contributing to the change in labor share, with a specific focus on the roles of ‘automation’ and ‘innovation in human tasks.’ We construct a general equilibrium model that distinctly incorporates both robot and non-robot capital to derive an econometric specification. Using task data from O*NET and employing the most recently developed sentence embedding tools to match tasks and patents, we construct a novel ‘innovation in human tasks’ variable for multiple countries. This allows us to empirically evaluate the impact of innovation in human tasks on labor share across countries for the first time in the literature. Our accounting analysis suggests that the positive influence of human task innovation outweighs the adverse effects of automation in most of countries we study. From our regression analysis, we estimate the elasticity of substitution between labor and non-robot capital to be less than one, while the elasticity of substitution between tasks is greater than one. With these estimates, we elucidate the direct and indirect effects of automation and innovation in human tasks on labor share

Automation, Human Task Innovation, and Labor Share: Unveiling the Role of Elasticity of Substitution

This paper investigates the elements contributing to the decline in labor share, with a specific focus on the roles of `automation' and `innovation in human tasks.' We construct a general equilibrium model that separately incorporates both robot and non-robot capital to derive an econometric specification. Based on regression results, we estimate the elasticity of substitution between labor and non-robot capital to be less than one, while the elasticity of substitution between tasks is greater than, but close to, one. Together with these estimates, our regression results yield three major findings. First, we identify two distinct channels through which robots affect labor share: automation and the decrease in the price of robots. Both channels are found to negatively impact labor share. Our general equilibrium model predicts that the effect of declining robot prices will intensify as robots become more prevalent. Second, we are the first to empirically evaluate the impact of human task innovation on labor share by constructing a novel index for new human tasks. Our accounting analysis suggests that the positive influence of human task innovation outweighs the adverse effects of automation. Lastly, by utilizing estimates of the elasticity of substitution between labor and non-robot capital, as well as between tasks, we elucidate the mechanisms through which factor prices affect the labor share. Specifically, we find that both the negative effect of automation and the positive effect of human task innovation are amplified through the aggregated task price channel

Automation, Human Task Innovation, and Labor Share: Unveiling the Role of Elasticity of Substitution

This paper investigates the elements contributing to the decline in labor share, with a particular focus on the roles of 'automation' and 'innovation in human-exclusive tasks.' We construct a general equilibrium model that separately incorporates both robot and non-robot capital to derive a regression equation. The regression results reveal four major findings. First, we identify two distinct channels through which robots influence labor share: automation and the reduction in robot prices. We find that both channels negatively impact labor share. Our general equilibrium model predicts that the effect of decreasing robot prices will intensify as robots become more prevalent. Second, we are the first to empirically assess the impact of innovation in human-exclusive tasks on labor share. Our findings suggest that the positive influence of human-exclusive innovation outweighs the adverse effect of automation. Third, we estimate that the elasticity of substitution between labor and non-robot capital is less than one, while the elasticity of substitution between tasks is greater than, but close to, one. Lastly, based on these estimates, we clarify the mechanisms by which the prices of factors —labor, robots, and non-robot capital— influence labor share. Specifically, we observe that both the negative effect of automation and the positive effect of human-exclusive task innovation are amplified through the aggregated task price channel

Automation, Human Task Innovation, and Labor Share: Unveiling the Role of Elasticity of Substitution

This study examines the global decline in labor share since the 2005, focusing on the impacts of robotic and human innovation within a general equilibrium framework. Using novel shift-share variables ---operational robot data, patent similarity to automation vocabularies, and cognitive task intensity scores--- the research addresses endogeneity issues across countries and sectors. Findings reveal that while human innovation positively impacts labor share, robotic innovation exerts a predominantly negative influence, largely offsetting human innovation's effects

Automation, Human Task Innovation, and Labor Share: Unveiling the Role of Elasticity of Substitution

This study examines the global decline in labor share since the 2005, focusing on the impacts of robotic and human innovation within a general equilibrium framework. Using novel shift-share variables ---operational robot data, patent similarity to automation vocabularies, and cognitive task intensity scores--- the research addresses endogeneity issues across countries and sectors. Findings reveal that while robotic innovation negatively impacts labor share, human innovation exerts a predominantly positive influence, largely offsetting automation's effects. Additionally, we find the elasticity of substitution between labor and capital is less than one, aligning with much of the literature. The paper acknowledges two primary limitations. First, the price factors are not exogenous. Second, fixed effects account for a significant proportion of the observed decline in labor share

Factors Influencing Labor Share: Automation, Task Innovation, and Elasticity of Substitution

This paper explores the underlying factors contributing to the recent decline in labor share, focusing specifically on the roles of automation and the development of new tasks that are exclusive to humans. First, our paper strengthens the argument that automation has a negative impact on labor share. Second, we are the first to empirically estimate the influence of new human-exclusive tasks on labor share. Our findings suggest that the positive impact of human-exclusive tasks dominates the negative impact brought about by automation. Third, we find that the elasticity of substitution between labor and capital is less than one, offering a coherent framework for predicting how various factors ---capital price, robot price, and wages--- impact labor share. We identify two distinct mechanisms through which robots negatively affect labor share: automation and a reduction in the price of robots. Our general equilibrium model predicts that the latter will gain increasing importance in the future as robots become more prevalent. Lastly, we estimate the elasticity of substitution between tasks to be one, empirically validating an assumption that many existing studies have made

Automation, Human Task Innovation, and Labor Share: Unveiling the Role of Elasticity of Substitution

This study examines the declining trend in global labor share across countries and sectors, focusing on the roles of robotic innovation (RI) and human innovation (HI). To address potential endogeneity, we construct instrumental variables using US patent data and large language models, calculating similarity scores between patent descriptions and robot descriptions for RI, and between patent descriptions and O*NET occupation descriptions for HI. Employing a general equilibrium model to derive our regression formula, our empirical findings reveal that RI negatively affects labor share, while HI has a positive impact. We estimate the elasticity of substitution between non-robot capital and labor to be less than one, aligning with most literature but differing from some previous studies