Rui Xu
Job Market Candidate

Stanford University
Department of Economics
579 Serra Mall
Stanford, CA 94305

(650) 391-8652


Curriculum Vitae

Macroeconomics, Labor Economics,
International Economics

Expected Graduation Date:
June, 2016

Thesis Committee:

Peter J. Klenow (Primary)

Charles I. Jones

Pablo Kurlat

Christopher Tonetti

Job Market Paper

High-Skilled Migration and Global Innovation

Science and engineering (S&E) workers are the fundamental inputs into scientific innovation and technology adoption. In the United States, more than 20% of the S&E workers are immigrants from developing countries. In this paper, I evaluate the impact of such brain drain from non-OECD (i.e., developing) countries using a multi-country endogenous growth model. The proposed framework introduces and quantifies a “frontier growth effect” of skilled migration: migrants from developing countries create more frontier knowledge in the U.S., and the non-rivalrous knowledge diffuses to all countries. In particular, each source country is able to adopt technology invented by migrants from other countries, a previously ignored externality of skilled migration. I quantify the model by matching both micro and macro moments, and then consider counterfactuals wherein U.S. immigration policy changes. My results suggest that a policy – which doubles the number of immigrants from every non-OECD country – would boost U.S. productivity growth by 0.1 percentage point per year, and improve average welfare in the U.S. by 3.3%. Such a policy can also benefit the source countries because of the “frontier growth effect”. Taking India as an example source country, I find that the same policy would lead to faster long-run growth and a 0.9% increase in average welfare in India. This welfare gain in India is largely the result of additional non-Indian migrants, indicating the significance of the previously overlooked externality.

Working Papers

Does Import Competition Induce More R&D? Evidence from U.S. Manufacturing Industries
(with Kaiji Gong)

We analyze the impact of rising Chinese import competition on innovative activities in the U.S. In particular, we adopt a similar identification strategy as in Autor et al (2013), whereby we use firm-level data and industry-level trade data to exploit cross-industry variation in exposure to import competition. We find that U.S. firms on average reduce R&D investment when faced with more import competition from China. The negative effect is driven by smaller, less productive or highly leveraged firms. Less R&D also seems to have adversely affected innovation output by U.S. firms in the form of patent application, in contrast to Bloom et. al. (2015) wherein import competition from China spurs innovation of European firms. Our findings cannot fully be explained by a simple story of negative demand shock, because R&D intensity – measured by R&D investment over sales in the previous year – also responded negatively to import competition. Further empirical and theoretical exploration is needed to provide explanations and to draw welfare implications.

Immigration and Top Income Inequality
Draft coming soon

Top income inequality rose sharply in the U.S. over the last 35 years. A majority of that can be accounted for by right-skewed salary income. Many hypotheses have been proposed to explain this phenomenon, including creative destruction by entrepreneurs and a decline in top tax rates. This paper proposes an additional channel through which highly skilled immigrants change the underlying talent distribution and thus raise top income inequality. This channel is supported by the empirical observation that immigrants are increasingly represented among top income earners. To quantify the magnitude of this channel, I construct a general equilibrium model with heterogeneous agents and polarized immigration flow as observed in the data. Based on my preliminary calculation, the change in immigration patterns can explain 10 – 15% of the observed rise in top income inequality in the U.S.

Exiting from Fragility: the Role of Fiscal Policies and Fiscal Institutions
(with Corinne Deléchat, Ejona Fuli, Dafina Mulaj and Gustavo Ramirez)
IMF working paper

This paper studies the role of fiscal policies and institutions in building resilience in sub-Saharan African countries during 1990-2013, with specific emphasis on a group of twenty-six countries that were deemed fragile in the 1990s. As the drivers of fragility and resilience are closely intertwined, we use both a probabilistic framework and GMM estimation to address endogeneity and reverse causality. We find that fiscal institutions and fiscal space, namely the capacity to raise tax revenue and contain current spending, as well as lower military spending and, to some extent, higher social expenditure, are significantly and fairly robustly associated with building resilience. Similar conclusions arise from a study of the progression of a group of seven out of the twenty-six sub-Saharan African countries that managed to build resilience after years of civil unrest and/or violent conflict.

Other Papers

Forecasting Intraday Volatility and Value-at-Risk with High-Frequency Data (with Mike K. P. So)
Asia-Pacific Finan Markets 20 (2013), p.83-111

In this paper, we develop modeling tools to forecast Value-at-Risk and volatility with investment horizons of less than one day. We quantify the market risk based on the study at a 30-min time horizon using modified GARCH models. The evaluation of intraday market risk can be useful to market participants (day traders and market makers) involved in frequent trading. As expected, the volatility features a significant intraday seasonality, which motivates us to include the intraday seasonal indexes in the GARCH models. We also incorporate realized variance (RV) and time-varying degrees of freedom in the GARCH models to capture more intraday information on the volatile market. The intrinsic tail risk index is introduced to assist with understanding the inherent risk level in each trading time interval. The proposed models are evaluated based on their forecasting performance of one-period-ahead volatility and Intraday Value-at-Risk (IVaR) with application to the 30 constituent stocks. We find that models with seasonal indexes generally outperform those without; RV can improve the out-of-sample forecasts of IVaR; student GARCH models with time-varying degrees of freedom perform best at 0.5 and 1% IVaR, while normal GARCH models excel for 2.5 and 5% IVaR. The results show that RV and seasonal indexes are useful to forecasting intraday volatility and Intraday VaR.