JMP专栏：UC-San Diego 第7期

为什么会有这个专栏？

有人曾问退休后的萨缪尔森：“能不能请您谈谈目前经济学研究的前沿是什么?”

萨翁答:“我不知道前沿在哪儿，如果我知道，我早就在哪儿了。这问题该去问我们的博士生。”

于是，我和小伙伴们在2023年底开启了一项“宏大”的计划，搜集整理美国经济系排名最靠前的10所高校博士生们的工作市场论文，这些论文一定程度上代表了经济学界的学术前沿。

后续，我们将每期推送3名候选人信息，直至全部候选人推送完毕。

本期是工作市场候选人信息第42期内容，也是UC-San Diego 第7期内容。

Ali Uppal

University of California San Diego

Research interests

Macroeconomics and Monetary Economics, Banking and Finance

Personal website

https://aliuppal.me/

Job market paper

Do Higher Interest Rates Make The Banking System Safer? Evidence From Bank Leverage

A vast theoretical literature claims that increasing interest rates reduces bank leverage, therefore making banks safer. The empirical validity of this claim is critical to improving our understanding of the transmission of monetary policy through banks in addition to informing the ongoing debate on whether monetary policy should be used to support financial stability. I show empirically that raising interest rates actually increases bank leverage. I propose and empirically validate a mechanism that explains the overall increase in bank leverage in response to monetary policy shocks which I term the loan-loss mechanism: contractionary shocks increase loan losses, reduce bank profits and equity, and ultimately increase bank leverage. I document why much of the theoretical literature is unable to explain the leverage response and develop a banking model where floating-rate loans entail a trade-off between interest rate risk and credit risk, which generates the loan-loss mechanism. Using microdata, I provide empirical evidence consistent with floating-rate loans hedging interest rate risk at the expense of generating loan losses.

Ha Vu

University of California San Diego

Research interests

Environmental Economics, Development Economics, Applied Microeconomics

Personal website

https://havudieu.wixsite.com/ha-vu

Job market paper

Export and Labor market outcomes: A supply chain perspective, evidence from Vietnam

Are the labor market changes from exports specific to exporting industries, or do they dissipate throughout the economy? To analyze this question, we study the case of Vietnam. Vietnam exported a total of $356B, making it the number 18 exporter in the world in 2021. Recent studies show provinces in Vietnam with greater exposure to tariff reductions observe greater rates of poverty decline and gains in wages and employment. We extend this literature by estimating the impact of exports propagated through domestic production linkages in Vietnam between 2010 and 2019. We find that higher export exposure leads to higher employment rate, lower inactivity, and an increase in female participation in the labor force. Furthermore, the empirical results also show that export increases wages and income as well as closes the gender wage gap and the college degree premium. Most of these results are larger in magnitude and different in direction of impact when accounting for input-output production structure of the economy underscoring the contributions of non-traded industries to export markets.

Jin Xi

University of California San Diego

Research interests

Econometrics of high-dimensional data, Macroeconomic forecasting

Personal website

https://jinxi.me/

Job market paper

Machine Learning using Nonstationary Data

Machine learning offers a promising set of tools for forecasting. However, some of the well-known properties do not apply to nonstationary data. This paper uses a simple procedure to extend machine learning methods to nonstationary data that does not require the researcher to have prior knowledge of which variables are nonstationary or the nature of the nonstationarity. I illustrate theoretically that using this procedure with LASSO or adaptive LASSO generates consistent variable selection on a mix of stationary and nonstationary explanatory variables. In an empirical exercise, I examine the success of this approach at forecasting U.S. inflation rates and the industrial production index using a number of different machine learning methods. I find that the proposed method either significantly improves prediction accuracy over traditional practices or delivers comparable performance, making it a reliable choice for obtaining stationary components of high-dimensional data.

供稿 | 翁煜晗

编辑 | 翁煜晗

审核 | 张毅