Selected publications

• Uncovering the key bilateral trade linkages in the U.S. domestic food supply chain through disruption simulations

  Journal of the Agricultural and Applied Economics Association · 2026-04-09

  articleOpen accessSenior author

  Abstract This paper uses counterfactual simulations to examine the impact of bilateral trade linkage disruptions on the U.S. agricultural and food trade system. Using an econometric gravity model, we estimate the relationship between disruptions and trade flows, identify critical state‐to‐state trade linkages whose disruption would significantly impact domestic trade and national welfare, and extend the analysis to the state level to measure the consequences of disruptions to individual states' interstate trade linkages. The findings offer valuable insights for mitigating the impacts of bilateral trade linkage disruptions and transport cost shocks on the U.S. agricultural and food trade system.

  PublisherDOI

• Farm-to-fork journey of your food: where does your salad come from?

  Figshare · 2026-04-08

  otherOpen access1st authorCorresponding

  Very excited to share this latest video on the food supply chain. For the first time, we can trace the farm-to-fork journey of all the ingredients needed for a specific meal.We started with a salad consumed in Champaign County, Illinois. You’ll see that some ingredients are local, while others come from faraway places.What should we focus on next? Pizza? Cheeseburger? Requests are welcome.

  PublisherDOI

• ENDID_IV

  Mendeley Data · 2026-01-28

  datasetOpen access1st authorCorresponding

  This repository reproduces the paper’s main empirical results from: “Difference-in-differences with endogenous externalities: Model and application to climate econometrics” Papers in Regional Science, 104 (2025) 100125. The code implements the paper’s strategy by combining (i) a PPML first stage to predict bilateral flows and construct a country–year exposure instrument, and (ii) an IV second stage to estimate causal effects on country–year outcomes. First stage (PPML). We estimate a Poisson model with high-dimensional fixed effects (exporter–importer and year) using fixest::fepois. From the estimated coefficients, we compute predicted bilateral flows and build WD_row_hat, an instrument that measures country i’s exposure in year t to shocks occurring in node j (e.g., drought). Exposure is weighted by shares derived from predicted bilateral flows, using either row-standardization (by exporter-year) or a year-global normalization, depending on the option selected. Second stage (IV). We estimate country–year panel models for outcomes such as ln_production (and optionally ln_area, ln_yield) as functions of observed exposure WD_row_obs, treated as endogenous and instrumented by WD_row_hat. Specifications include fixed effects and climate/structural controls (temperature, precipitation, irrigation, and drought indicators), estimated with fixest::feols using its IV formula syntax. Inference. Standard errors are obtained via a two-level bootstrap: (1) draw PPML coefficient vectors from a multivariate normal approximation based on the first-stage variance–covariance matrix; (2) resample country clusters (or a user-defined cluster key) with replacement and re-estimate the second-stage and IV models for each replication. This yields bootstrap standard errors that incorporate both first-stage uncertainty and within-cluster dependence. Parallelization uses foreach/doParallel, and reproducibility is ensured via doRNG with a fixed seed. Inputs are: (a) a bilateral panel (isoi, isoj, year) for the PPML stage and (b) a country–year panel (iso, year) for the IV stage. Running the replication script produces point estimates and a coefficient table with bootstrap standard errors and key diagnostics (e.g., first-stage F-statistics when available).

  PublisherDOI

• ENDID_IV

  Mendeley Data · 2026-01-26

  datasetOpen access1st authorCorresponding

  This repository reproduces the paper’s main empirical results from: “Difference-in-differences with endogenous externalities: Model and application to climate econometrics” Papers in Regional Science, 104 (2025) 100125. The code implements the paper’s strategy by combining (i) a PPML first stage to predict bilateral flows and construct a country–year exposure instrument, and (ii) an IV second stage to estimate causal effects on country–year outcomes. First stage (PPML). We estimate a Poisson model with high-dimensional fixed effects (exporter–importer and year) using fixest::fepois. From the estimated coefficients, we compute predicted bilateral flows and build WD_row_hat, an instrument that measures country i’s exposure in year t to shocks occurring in node j (e.g., drought). Exposure is weighted by shares derived from predicted bilateral flows, using either row-standardization (by exporter-year) or a year-global normalization, depending on the option selected. Second stage (IV). We estimate country–year panel models for outcomes such as ln_production (and optionally ln_area, ln_yield) as functions of observed exposure WD_row_obs, treated as endogenous and instrumented by WD_row_hat. Specifications include fixed effects and climate/structural controls (temperature, precipitation, irrigation, and drought indicators), estimated with fixest::feols using its IV formula syntax. Inference. Standard errors are obtained via a two-level bootstrap: (1) draw PPML coefficient vectors from a multivariate normal approximation based on the first-stage variance–covariance matrix; (2) resample country clusters (or a user-defined cluster key) with replacement and re-estimate the second-stage and IV models for each replication. This yields bootstrap standard errors that incorporate both first-stage uncertainty and within-cluster dependence. Parallelization uses foreach/doParallel, and reproducibility is ensured via doRNG with a fixed seed. Inputs are: (a) a bilateral panel (isoi, isoj, year) for the PPML stage and (b) a country–year panel (iso, year) for the IV stage. Running the replication script produces point estimates and a coefficient table with bootstrap standard errors and key diagnostics (e.g., first-stage F-statistics when available).

  PublisherDOI

• ENDID_IV

  Open MIND · 2026-01-01

  dataset1st authorCorresponding

  This repository reproduces the paper’s main empirical results from: “Difference-in-differences with endogenous externalities: Model and application to climate econometrics” Papers in Regional Science, 104 (2025) 100125. The code implements the paper’s strategy by combining (i) a PPML first stage to predict bilateral flows and construct a country–year exposure instrument, and (ii) an IV second stage to estimate causal effects on country–year outcomes. First stage (PPML). We estimate a Poisson model with high-dimensional fixed effects (exporter–importer and year) using fixest::fepois. From the estimated coefficients, we compute predicted bilateral flows and build WD_row_hat, an instrument that measures country i’s exposure in year t to shocks occurring in node j (e.g., drought). Exposure is weighted by shares derived from predicted bilateral flows, using either row-standardization (by exporter-year) or a year-global normalization, depending on the option selected. Second stage (IV). We estimate country–year panel models for outcomes such as ln_production (and optionally ln_area, ln_yield) as functions of observed exposure WD_row_obs, treated as endogenous and instrumented by WD_row_hat. Specifications include fixed effects and climate/structural controls (temperature, precipitation, irrigation, and drought indicators), estimated with fixest::feols using its IV formula syntax. Inference. Standard errors are obtained via a two-level bootstrap: (1) draw PPML coefficient vectors from a multivariate normal approximation based on the first-stage variance–covariance matrix; (2) resample country clusters (or a user-defined cluster key) with replacement and re-estimate the second-stage and IV models for each replication. This yields bootstrap standard errors that incorporate both first-stage uncertainty and within-cluster dependence. Parallelization uses foreach/doParallel, and reproducibility is ensured via doRNG with a fixed seed. Inputs are: (a) a bilateral panel (isoi, isoj, year) for the PPML stage and (b) a country–year panel (iso, year) for the IV stage. Running the replication script produces point estimates and a coefficient table with bootstrap standard errors and key diagnostics (e.g., first-stage F-statistics when available).

• ENDID_IV

  Open MIND · 2026-01-01

  dataset1st authorCorresponding

  This repository reproduces the paper’s main empirical results from: “Difference-in-differences with endogenous externalities: Model and application to climate econometrics” Papers in Regional Science, 104 (2025) 100125. The code implements the paper’s strategy by combining (i) a PPML first stage to predict bilateral flows and construct a country–year exposure instrument, and (ii) an IV second stage to estimate causal effects on country–year outcomes. First stage (PPML). We estimate a Poisson model with high-dimensional fixed effects (exporter–importer and year) using fixest::fepois. From the estimated coefficients, we compute predicted bilateral flows and build WD_row_hat, an instrument that measures country i’s exposure in year t to shocks occurring in node j (e.g., drought). Exposure is weighted by shares derived from predicted bilateral flows, using either row-standardization (by exporter-year) or a year-global normalization, depending on the option selected. Second stage (IV). We estimate country–year panel models for outcomes such as ln_production (and optionally ln_area, ln_yield) as functions of observed exposure WD_row_obs, treated as endogenous and instrumented by WD_row_hat. Specifications include fixed effects and climate/structural controls (temperature, precipitation, irrigation, and drought indicators), estimated with fixest::feols using its IV formula syntax. Inference. Standard errors are obtained via a two-level bootstrap: (1) draw PPML coefficient vectors from a multivariate normal approximation based on the first-stage variance–covariance matrix; (2) resample country clusters (or a user-defined cluster key) with replacement and re-estimate the second-stage and IV models for each replication. This yields bootstrap standard errors that incorporate both first-stage uncertainty and within-cluster dependence. Parallelization uses foreach/doParallel, and reproducibility is ensured via doRNG with a fixed seed. Inputs are: (a) a bilateral panel (isoi, isoj, year) for the PPML stage and (b) a country–year panel (iso, year) for the IV stage. Running the replication script produces point estimates and a coefficient table with bootstrap standard errors and key diagnostics (e.g., first-stage F-statistics when available).

• Farm-to-fork journey of your food: where does your salad come from?

  Figshare · 2026-04-08

  otherOpen access1st authorCorresponding

  Very excited to share this latest video on the food supply chain. For the first time, we can trace the farm-to-fork journey of all the ingredients needed for a specific meal.We started with a salad consumed in Champaign County, Illinois. You’ll see that some ingredients are local, while others come from faraway places.What should we focus on next? Pizza? Cheeseburger? Requests are welcome.

  PublisherDOI

• Impact of Extreme Weather Events on the U.S. Domestic Supply Chain of Food Manufacturing

  Figshare · 2026-01-01

  otherOpen access1st authorCorresponding

  Yim H., Dall’erba S. (2025) Impact of Extreme Weather Events on the U.S. Domestic Trade and Supply Chain of Food, <i>Proceedings of the National Academy of Sciences, </i>122, 41, e2424715122.<b>https://doi.org/10.1073/pnas.2424715122</b>

  PublisherDOI

• The Impact of Autonomous Truck Technology on US Interstate Trade

  Journal of Regional Science · 2026-05-04

  articleOpen access

  ABSTRACT Recent advances in autonomous and semi‐autonomous vehicle technologies promise substantial cost savings for goods shipped by truck. In this study, we quantify the impacts of these transport cost reductions on the US interstate trade using a structural gravity model of domestic trade. Based on projected cost savings from the widespread adoption of self‐driving technologies, we estimate significant increases in total interstate trade value. State‐level impacts vary from 40.3% of GDP in Mississippi to 5.9% in Florida, while the largest impacts in dollar value are observed in Texas and New York. The sectoral analysis highlights motorized vehicles, mixed freight, and electronics as the industries experiencing the largest trade value growth. Additionally, goods with low value‐to‐weight ratios—where shipping costs represent a large share of the delivered value—are expected to benefit most in relative terms. These findings underscore the transformative potential of autonomous vehicle technologies in reshaping US trade patterns and sectoral dynamics.

  PublisherDOI

• Impact of Extreme Weather Events on the U.S. Domestic Supply Chain of Food Manufacturing

  Figshare · 2026-01-01

  otherOpen access1st authorCorresponding

  Yim H., Dall’erba S. (2025) Impact of Extreme Weather Events on the U.S. Domestic Trade and Supply Chain of Food, <i>Proceedings of the National Academy of Sciences, </i>122, 41, e2424715122.<b>https://doi.org/10.1073/pnas.2424715122</b>

  PublisherDOI