Recent economic experiences have demonstrated the importance of understanding departures from frictionless markets and perfect information-processing for the field of macroeconomics. The global financial crisis has highlighted the importance of financial frictions for macroeconomic policy. With conventional monetary policy unable to stabilize the economy in the wake of the global financial crisis, central banks turned to unconventional tools. Understanding how these tools worked through interactions with financial market disruptions is crucial for designing and implementing policies to deal with the next crisis. The first two chapters show theoretically how unconventional policy worked, and test these predictions in the data.

The rise of political polarization over the past decades raises important questions about how households form macroeconomic beliefs, and how this departs from the typical rationality assumptions embedded in textbook macroeconomic models. The final chapter shows theoretically how imperfect information-processing leads to a divergence of macroeconomic beliefs across households. Empirically, growing disagreement about macroeconomic outcomes is found in survey data; moreover, this disagreement leads to differential consumptions decisions following political shocks.

Chapter 1 embeds a model of the term structure of interest rates featuring market segmentation and limits to arbitrage within a New Keynesian model to study unconventional monetary policy. Because the transmission of monetary policy depends on private agents with limited risk-bearing capacity, financial market disruptions reduce the efficacy of both conventional policy as well as forward guidance. Conversely, financial crises are precisely when large scale asset purchases are most effective. Policymakers can take advantage of the inability of financial markets to fully absorb these purchases, which can push down long-term interest rates and help stabilize output and inflation.

Chapter 2 seeks to understand empirically the effects of large-scale asset purchase programs recently implemented by central banks. In joint work with Yuriy Gorodnichenko, we study how markets absorb large demand shocks for risk-free debt. Using high-frequency identification, we exploit the structure of the primary market for U.S. Treasuries to isolate demand shocks. These shocks are sizable, leading to large movements in Treasury yields and impacting corporate borrowing rates. Informed by a preferred habitat model of the term structure, we test for "local" demand effects and find evidence consistent with theoretical predictions. Crucially, this local effect is strongest when financial markets are disrupted. Our estimates are consistent with the view that quantitative easing worked mainly via market segmentation, with a potentially limited role for other channels.

Chapter 3 explores the role of political polarization in shaping the economic expectations and consumption behavior of households. In joint work with Rupal Kamdar, we first develop a rational inattention model in which heterogeneous households must decide how to obtain information. Theoretically, we show there is a "paradox of information" where falling information costs exacerbate disagreement. Next, using survey data, we find evidence that political polarization has increased dispersion in macroeconomic beliefs. Disagreement is particularly acute following a general election when the presidential party switches; moreover, this effect has been increasing since the 1980s. Finally, we also find that polarization feeds into consumption decisions. Using high-frequency spending data at the zip code level in California, we find Republican-leaning regions exhibit substantially larger consumption following the 2016 election.

This study fills some of the gaps in the literature on resource misallocation in Indian manufacturing. There are three key contributions in this study. The first is the proper measurement of misallocation in Indian manufacturing. This is achieved by incorporating data from both the formal and informal sectors and intermediate inputs in calculating misallocation. By using value added based measures of revenue productivity (TFPR) we find that there was greater misallocation in the formal sector compared to the informal sector in the period 1994-2005. We also find that resource misallcoation had increased in the formal sector during this period. Using a monopolistic competitive model with heterogeneity in firm productivity and including intermediate inputs in the production function we show that resource misallocation at the industry level can be amplified due to intermediate inputs. We show that the value added based measures of misallocation suffer from omitted variable bias and that the magnitude and sign of this bias is dependent on the covariance of the distortions to intermediate inputs and other factor inputs.

The second contribution is towards identifying the nature, magnitude and sources of misallocation. Analyzing distortions to factor inputs individually we find that the distortions to intermediate inputs to be the largest followed by the distortions to capital. We find that distortions to intermediate inputs are larger in the informal sector than in the formal sector. Using qualitative information on informal firms we find that misallocation is positively correlated with shortage of capital and poor access to intermediate inputs and negatively correlated with firm market share.

The third contribution is that we measure the magnitude of misallocation caused by a specific size dependent policy in India. A small scale firm in India can get up to 100% exemption from paying excise tax if its annual turnover is less than Rs. 10 million. Using data on formal manufacturing firms in India we show that such an exemption encourages firms to operate on a sub-optimal scale. We construct the counterfactual distribution of output without any tax distortion using maximum likelihood and non-linear least squares estimation methods. Using the empirical estimates of the scale parameter in the literature we find that there are output gains in the range of 13% to 230% under a revenue neutral flat tax regime. These gains are sensitive to the scale parameter of the production function and are explosive as the scale parameter approaches one.

Does inequality react to stabilization policies and macroeconomic shocks at business cycle frequencies? Does an unanticipated innovation in inequality impact aggregate demand and drive cyclical fluctuations? Does the level of inequality influence the propagation of stabilization policies? To answer these questions, I first investigate which factors of earnings distributions are represented by a measure of inequality in Chapter 1. Chapter 2 develops an econometric tool to evaluate the contribution of macroeconomic shocks to the dynamics of an endogenous variable of interest. Finally, Chapter 3 deals with the questions above.

In Chapter 1, I derive principal components of log earnings distributions in the U.S. and propose a simple three-factor model to rationalize my empirical results. Using data on earnings distribution in the U.S. from 1978 to 2013, I find that more than 90 percent of the total variation in the distribution can be summarized by two underlying factors, which are related to the location and dispersion of the distribution, where the dispersion factor is tightly associated with the log P90/P10 index. Moreover, most of the remaining portion is due to another factor characterizing asymmetric components. To rationalize these findings, I suggest asymmetric Laplace distributions as a model of log earnings distributions. In this model, the right-tail of earnings always follows a Pareto distribution, unlike log normal distributions. Furthermore, it is a tractable distributional family featuring three parameters representing the location, dispersion, and degree of asymmetry, respectively. I describe the dynamics of those parameters in the U.S. both in trends and concerning business cycles. Finally, I illustrate how a conventional Gaussian AR(1) model for individual log earnings can be easily modified to admit asymmetric Laplace distributions.

Chapter 2 is based on joint work with Yuriy Gorodnichenko, which is forthcoming in Journal of Business and Statistics under the same title. We propose and study properties of an estimator of the forecast error variance decomposition in the local projections framework. We find for empirically relevant sample sizes that, after being bias-corrected with bootstrap, our estimator performs well in simulations. We also illustrate the workings of our estimator empirically for monetary policy and productivity shocks.

Chapter 3 deals with questions on the relationship between business cycles and earnings inequality. For an empirical investigation, I construct a novel, high-quality, quarterly measure of earnings inequality and document the following facts. First, an expansionary productivity shock and a contractionary government expenditure shock reduce earnings inequality significantly at the medium-run, while monetary policy shocks have little effects. Second, an unanticipated positive innovation in earnings inequality, which summarizes redistribution from the poor to the rich, lowers aggregate demand substantially in a U-shaped manner. Lastly, the power of stabilization policies increases with the level of inequality. To rationalize these results, I develop a tractable, theoretical framework. I analytically illustrate that inequality in a simple two-agent model is related to demand shocks in a representative agent framework. To match the shape and magnitude of the empirical impulse responses, I further introduce new features including countercyclical earnings risk, an endogenous extensive margin of being credit constrained, and decreasing relative risk aversion preferences.

In the introductory chapter of this dissertation, I motivate my use of disaggregate, cross-sectional variation for studying the effects of various policies on the aggregate economy. In particular, I argue that the topics empirically studied in this dissertation are historically unprecedented in nature and therefore necessitate a cross-sectional approach. I briefly review the anatomy of the linear least squares estimator to illustrate how standard cross-sectional designs can only recover the relative effects of policies. I then preview the theoretical tools I use in this dissertation to make the mapping between cross-sectional estimates and aggregate effects explicit.

In the second chapter of my dissertation, I generalize a textbook currency union model to incorporate trade in intermediates among labor markets in order to study the local, spillover, and aggregate effects of government spending. This theory provides a theoretical framework for interpreting the empirical, cross-sectional moments estimated in the subsequent chapter. The spillover effects of government spending mediated by trade in intermediates represents a novel and understudied mechanism by which local fiscal multiplier estimates likely represent a lower bound on the aggregate, Zero Lower Bound (ZLB) fiscal multiplier. In my framework, there is both a local and a spillover (relative) multiplier of government spending. Theoretically, summing both multipliers together yields an approximate lower bound on the aggregate, ZLB fiscal multiplier.

In the third chapter, I use geographic variation in government spending under the 2009 Recovery Act and import-export linkages between states from the 2007 Commodity Flow Survey to estimate a local relative multiplier of 1.46 and a spillover relative multiplier of 1.33. Adding both together yields an approximate lower bound on the aggregate, ZLB fiscal multiplier of 2.8, nearly doubling the lower bound implied by the local multiplier estimate alone. A sectoral decomposition of both estimated multipliers strongly corroborates the trade in intermediates spillover mechanism.

The final chapter studies the relative and aggregate economic effects of a prominent policy intervention implemented around the world during the COVID-19 pandemic by similarly interpreting causally identified, cross-sectional estimates through the lens of a currency union model. In particular, the high-frequency, decentralized implementation of Stay-at-Home orders in the U.S. is used to disentangle the labor market effects of SAH orders from the general economic disruption wrought by the COVID-19 pandemic. The analysis implies that each week of SAH exposure increased a state's weekly initial unemployment insurance (UI) claims by 1.9% of its employment level relative to other states. A back-of-the-envelope calculation implies that, of the 17 million UI claims between March 14 and April 4, only 4 million were attributable to SAH orders. A currency union model is developed to provide conditions for mapping this estimate to aggregate employment losses attributable to such orders.