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Essays on Applied Econometrics and Causal Inference: Applications to the Analysis of the Tax Multiplier and to the Evaluation of Online Lending Market

Abstract

The dissertation consists of three chapters, with emphasis on analyzing macro- and micro-level data and applying econometric techniques so as to measure treatment effects and draw a causal inference.

The first chapter estimates the causal impacts of tax policy changes on the US post-war macroeconomy by exploring the permanent and exogenous tax changes identified by the narrative approach. In a state-dependent model estimated by local projections method, the tax multiplier effects are allowed to differ conditional on the contemporary economic conditions. The empirical findings show that the impulse response of output to tax shocks is significantly weaker and more sluggish when the US economy is in a recession than when it is in an expansion. I also find evidence suggesting that such asymmetry in the multiplier impacts happens as the result of a relatively smaller and slower response of private investment to tax policy changes during periods of recession. While no strong difference is found regarding the impulse responses of private consumption to tax changes under different economic conditions. These results are robust with respect to alternative model assumptions and specifications. Thus, the research indicates that tax policies constitute a more effective fiscal tool for economic adjustment in a booming economy instead of a dwindling one.

The second and third chapters are joint work with Baizhu Chen and study the fast-growing online lending market. In the second chapter, we estimate the effect of credit insurance on the online peer-to-peer (P2P) lending market. Online lending is a fast growing area of alternative finance. However, it is often plagued by asymmetric information problems and high credit risk, especially in developing countries that do not have well-established financial and credit rating systems. To address this issue, some Chinese P2P marketplaces have incorporated loan insurance into their online platforms. We estimate the treatment effects of credit insurance on the P2P market by exploiting a unique quasi-experiment. Specifically, loan guarantees gradually became available on a top Chinese P2P lending platform to borrowers from 31 major cities between 2012 and 2014 through 12 waves of business expansion. Our empirical results suggest that the availability of credit insurance resulted in significant, strong, and persistent treatment effects on the market demand and supply. The adoption of credit insurance was associated with dramatic increases in the number of loan listings, funding probability per loan, and bidding amount per lender. The average funding time per loan decreased by as large as about 170 hours.

The third chapter utilizes a unique dataset of P2P lending with detailed loan and borrower information to study which borrower characteristics lenders value when choosing loans to fund and whether lenders value characteristics that minimize the probability of default. We begin by presenting a theoretical model (1) to help illustrate the behavior of lenders and borrowers in a P2P market and (2) to derive a reduced form model for empirical analysis. In the model, lenders make ex ante funding decisions to maximize their subjectively expected returns while borrowers decide whether or not to default ex post. The subjective expectation is formulated based on the observed borrower characteristics and loan information. As a result, we can evaluate lenders' funding decision by comparing for different borrower traits the associations to funding and default probabilities. In the empirical part, the online enables unbiased estimation of borrower characteristics that lenders favor as we observe all the information that lenders know about their borrowers. However, estimating characteristics that predict loan default is problematic due to selection at the funding stage. To address this issue, we exploit the exogenous variation in the probability of funding caused by contemporaneous competition on the platform. The results imply that P2P lenders consider employment borrower characteristics as most important when making funding decisions, but disregard several characteristics that are valuable predictors of default risk. Specifically, lenders overestimate the importance of verified employment information and underestimate the importance of verified education level and marital status.

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