Causal Inference on Jeremy Meng

UC Davis

Mon, 01 Dec 2025 02:41:51 +0000

International economics has long been a field with restricted entry. Entry has been restricted by such devices as “offer curves,” for which only the initiated know which is whose, but also by the sheer variety of models and effects that appear much more abundant than in other areas of applied economics. – by Robert Mundell

I am so lucky and proud to be perhaps the last generation of international economists to be trained and to have worked on all three areas: international macroeconomics, international finance, and international trade.

Economist (UC Davis Health)

Fri, 10 Oct 2025 04:00:59 +0000

In 2025, I worked as an Economist/Data Scientist with Manho Kang, Kadee Russ, and James Waters on a research project evaluating how Trump’s trade policy affected the cost of imported medical goods and the U.S. healthcare system.

Built a novel product-level trade policy database by extracting and standardizing information from 10,000+ pages of legal documents, creating a dataset with 10M+ records to track daily tariff changes across medical goods and source countries.
Developed an original procedure to identify roughly 300 medical product categories from more than 10,000 import classification codes, improving the accuracy and granularity of healthcare trade policy measurement.
Designed causal inference models to estimate tariff pass-through, demand elasticity, heterogeneous impacts across product groups, and the overall cost burden of trade policy on the U.S. healthcare system.
Created documentation, summary tables, and analytical outputs that translated complex legal, trade, and statistical information into clear evidence for economists, policy researchers, and healthcare stakeholders.

Applied Econometrics

Sun, 19 Feb 2012 18:44:16 +0000

I am an expert in a wide range of tools in applied econometrics for predictive and cause-and-effect types of questions using various data types, including spatial panel, longitudinal data, and time series.

I am interested in collecting and constructing my own data, often being the first in specific domains, to best answer the questions at hand.

Although frameworks like propensity score matching, staggered diff-in-diffs, event studies, and synthetic controls are in my arsenal, I am interested in deeply understanding the nature of the problem and discovering natural experiments in observational data to understand causes and effects.

Economic Impacts of Trump's 2025 Tariffs

Thu, 01 Jan 2026 02:04:23 +0000

In this research project, I created a new procedure to reliably track daily changes in trade policy, explicitly distinguishing between detailed timing up to the minute of announcement and implementation. This comes from processing 10K+ pages of trade documents.

Focusing on medical goods, we estimate the tariff pass-through (i.e. demand elasticity in a broader sense) in a diff-in-diffs framework.

Abstract: Have recent tariffs resulted in increased costs for the US healthcare system? We examine US trade data and compile a database of statutory tariff changes. Tariffs on medical goods narrowly defined resulted in $3.4 Billion in duties assessed between February and July 2025–more than 10 times the same period in 2024, with a 55.8 percent rate of pass-through at the US border. We estimate that had medical goods imports observed in 2024 been subject to the statutory tariff levels prevailing in August 2025, assessed duties would have been $15.8 Billion, 30 times higher than those observed in real-time.
The replication codebase is open access here.
A local copy is also available here.

Benchmark Inclusion and Sovereign Risk

Sat, 01 Mar 2025 02:17:20 +0000

I study how financial benchmark index inclusion affects sovereign bond markets and sovereign risk. The project examines whether being included in widely followed bond indices changes investor demand, capital flows, and borrowing costs for sovereign governments. A local copy is open access here.

Built a country-month panel dataset combining sovereign bond yields, sovereign spreads, benchmark index membership, index weights, and mutual fund holdings data. The dataset links global investment benchmarks to actual portfolio allocation decisions and sovereign debt market outcomes.
Used event study methods to examine how sovereign bond markets respond around benchmark inclusion and exclusion events. This approach helps identify whether changes in index status are associated with shifts in bond yields, spreads, and investor demand.
Developed a shift-share instrumental variable strategy based on benchmark exposure and global index-tracking flows. This method uses variation in countries’ exposure to benchmark-driven demand to estimate the effect of foreign investor demand on sovereign borrowing costs.
Estimated foreign demand elasticity for sovereign bonds by analyzing how global investors reallocate portfolios when benchmark weights change. This helps quantify how sensitive sovereign debt prices are to changes in institutional investor demand.
Examined spillover and network effects across countries by studying how index-driven capital flows affect not only included countries, but also countries that compete for global portfolio investment. This highlights the broader market consequences of benchmark-based investing.
Connected the empirical analysis to a sovereign credit risk framework to interpret how financial integration, investor demand, and benchmark inclusion can affect sovereign risk premia.

Fiscal Spending and Growth: New Evidence from a Natural Experiment

Thu, 02 Jun 2022 22:55:34 +0000

In this paper, I study the long-term economic effects of temporary government spending shocks by using the surge in U.S. government procurement during World War I as a natural experiment. The project asks whether short-lived fiscal spending can create persistent improvements in regional economic activity, or whether its effects disappear once the spending shock ends.

Digitized over 10,000 historical procurement contracts to construct original state-level measures of government spending exposure during World War I.
Used cross-state variation in wartime procurement spending as a natural experiment to estimate the causal effect of temporary fiscal shocks on regional growth.
Built a state-level historical panel linking government spending shocks to subsequent changes in personal income, employment, and regional economic outcomes.
Estimated the dynamic effects of government spending over time, showing that temporary spending shocks led to sustained increases in personal income and employment.
Found that the effects persisted even seven years after the initial spending shock, suggesting that temporary fiscal interventions can generate longer-lasting regional growth.
Contributed new evidence to the debate on fiscal multipliers, regional development, and whether government spending can have persistent effects beyond the short-run demand response.