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This dissertation studies the effects macroeconomic policies and events on economic outcomes and welfare, using a combination of empirical analysis and quantitative modeling. The first chapter examines the effects of negative interest rate policies implemented by central banks in the aftermath of the Great Recession. The second chapter studies the timing of the Industrial Revolution and the sources of business cycle fluctuations prior to the Great Depression. Both chapters contribute to our understanding of how economic policies and events impact economic welfare. The first chapter studies the impact of negative interest rate policies on bank lending, investment, and employment, taking into account the role of capital-labor substitution in production. Using matched firm-bank data from seven euro area countries and employing a difference-in-differences approach, I find that following the introduction of these policies, firms linked to banks with higher deposit ratios receive less credit relative to their counterparts associated with banks with lower deposit ratios. These firms also invest less but tend to hire more employees, especially in industries with high capital-labor substitutability. These findings highlight the role of capital-labor substitution in shaping the effects of negative interest rate policies. To further analyze these findings in a general equilibrium framework and to quantify the aggregate effects of these policies, I use a DSGE model that incorporates bank lending and a CES production function. I find that negative interest rate policies increase lending, investment, employment, and welfare in consumption equivalent units. This model also reveals that higher capital-labor substitutability surprisingly leads to larger declines in output and bank equity following a negative capital productivity shock. Based on this insight, I show that welfare gains from implementing negative interest rate policies increase with capital-labor substitution, and even

This dissertation contributes toward our understanding of how technological changes in recent decades affect firm behaviors and various aspect of the aggregate economy, including changes in market concentration, productivity, labor share and credit allocation. In Chapter 1, “Customer Acquisition, Rising Concentration and US Productivity Dynamics”, I document that the cost of marketing and advertising has declined enormously in recent decades due to the advance of digital technologies. This chapter then studies the macroeconomic consequences of lower marketing cost, and finds that it is a critical driving force of several striking macroeconomic trends, including rising market concentration and productivity growth slowdown since the 1990s. I develop an endogenous growth model with product market search frictions. Firms invest in innovation and marketing to build customer base, which is a long-term asset. Then I exogenously feed in the observed large drop of marketing cost into the quantitative model and find that it accounts for 83\% of the rise in market concentration, measured by the largest firm’s market share. Cheaper marketing generates a positive level effect and a negative growth effect on productivity. These two effects together explain around 1/3 of the decline in productivity growth rate and successfully capture its hump-shaped pattern over time. Finally, I conduct a welfare analysis and find that firms tend to over-invest in marketing compared to the socially optimal allocation, which implies that welfare can be improved by a marketing tax.In Chapter 2, “Revisiting Capital-Skill Complementarity, Inequality, and Labor Share”, jointly written with Lee Ohanian and Musa Orak, we analyze the quantitative contribution of capital-skill complementarity in accounting for rising wage inequality, as in Krusell, Ohanian, Rios-Rull, and Violante (KORV, 2000). We study how well the KORV framework accounts for more recent data, including the large changes in labor’s share

While the impact of agent heterogeneity has long been recognized in the Economicliterature, the link between agent heterogeneity and international asset pricing is yet to be fully understood. In this dissertation I use an overlapping generations framework to study the impact that agent heterogeneity in risk aversion has on the real exchange rate determination. Chapter 1 presents and develops the theoretical model used to study the implications of agent heterogeneity in risk aversion on the real exchange rate. I introduce a twocountry model that features heterogeneous risk aversion profiles for agents, both within and between countries. Furthermore, it is shown that the model can explain the Cyclicality puzzle documented in Backus and Smith (1993), which highlights the empirical disconnect between the exchange rate and relative consumption growth. This chapter also presents the numerical outcome of the model. Chapter 2 explains the quantitative methodology used to code and find the numerical solution of the model presented in chapter 1. The model does not admit a closed form solution and thus the presented outcome relies on the application of Monte Carlo Methods, the Feynman-Kac Theorem and the Piccard Iteration Theorem. Finally, Chapter 3 presents recent empirical evidence on the Cyclicality puzzle between the US and 4 OECD countries: UK, France, Germany and Italy.

In Chapter 1, I use a structural approach, to quantify the effect of land-use regulations on different age and education groups. I estimate a dynamic spatial structural equilibrium model of household location choice, local housing supply, and amenity supply. I show that in the long-run, removing land-use restrictions benefits all household groups and increases aggregate consumption by 7.1%. These consumption gains vary across households, less educated and younger households see increases in consumption about twice as large as more educated or older households. In contrast, in the short-run, removing land-use regulations reduces the consumption of older-richer homeowners while increasing the consumption of younger renters. In a counterfactual 1990-2019 transition, abolishing land-use regulations reduces the consumption of households born before the mid-1960s, while increasing consumption of more recent generations. In Chapter 2, co-authored with Mahyar Kargar, Benjamin Lester, Shuo Liu, Pierre-Olivier Weill, Diego Zuniga, we study liquidity conditions in the corporate bond market during the COVID-19 pandemic. We document that the cost of trading immediately via risky-principal trades dramatically increased at the height of the sell-off, forcing customers to shift toward slower agency trades. Exploiting eligibility requirements, we show that the Federal Reserve’s corporate credit facilities have had a positive effect on market liquidity. A structural estimation reveals that customers’ willingness to pay for immediacy increased by about 200 bps per dollar of transaction, but quickly subsided after the Fed announced its interventions. Dealers’ marginal cost also increased substantially but did not fully subside.In Chapter 3, co-authored with Diego Zuniga, we study inter-dealer trading patterns in the US corporate bond market. We document that dealers trade with only a small group of other dealers and that this group of dealers is highly persistent over time. We show tha

New developments in computational power and methods have introduced a wide variety of new tools for economic analysis. This dissertation explores whether these new advances can help us understand and analyze macroeconomic phenomena.Chapter 1 shows how recently developed clustering methods can be helpful in identifying consumer groups based on a history of purchasing behavior. While traditional methods of customer segmentation rely on observable characteristics of consumers or the products they buy, the methods I use in this chapter rely instead on identifying groups of consumers who buy a similar set of products. If consumers who buy similar products are likely to have similar preferences, then clustering groups of consumers who buy similar products can potentially uncover groups of consumers who share unobservable characteristics that drive their preference structures without explicitly specifying those preference structures. Using simulations of a discrete choice logit demand system, I show that a density peaks clustering method can effectively uncover consumers with different preferences, using a series of examples to show how different assumptions impact the effectiveness of the clustering algorithm.In chapter 2, I apply the methods of chapter 1 to attempt to improve measurements of the cost of living. In particular, I show that methods for measuring cost of living that rely on aggregate CES representative agents will often overstate the gains from new product varieties when groups of consumers have different tastes for products. Since the purchase data of consumers shows that there is substantial heterogeneity in the sets of products consumers buy, estimating inflation using a representative agent approach could produce biased estimates. However, the methods from chapter 1 can help to mitigate the bias from heterogeneity by grouping similar consumers based on their purchase history. I apply the method to a large panel of consumers and show that clustering consu