Research
Working Papers
Do Productivity Shocks Cause Inputs Misallocation?
Under Review
This paper investigates how productivity dispersion relates to input misallocation using a model with staggered productivity shocks that create wedges between anticipated and realized productivity for any production input. With inputs allocated optimally ex ante but suboptimally ex post, dispersion in realized productivity contributes to ex post input misallocation. Analyzing European firm data from 2000–2017 reveals significant co-movement between productivity dispersion and capital/labor misallocation across industries. Productivity dispersion explains a substantial share of capital and labor misallocation (40% and 70%), and 10% of materials misallocation, confirming its key role in allocation frictions.
Conference Presentations:
2024: CAED (University Park, PA), EEA-ESEM (Rotterdam, NL), EARIE - Rising Star Session (Amsterdam, NL)
2023: 12th CompNet Annual Conference (Bruxelles, BE)
2022: MICROPROD - Final event (Bruxelles, BE)
In Search of (Factor-Biased) Learning by Exporting
Joint with Joonkyo Hong
Under Review
Exporting plants often undergo significant technology upgrades, becoming more productive than their domestic counterparts. This process, called learning by exporting, is usually modeled as a Hicks-neutral TFP shifter, overlooking factor-biased technical improvements. We develop a dynamic model of production, exporting, and capital investment that incorporates factor-augmenting efficiencies.We find that exporting increases TFP by 9%, skilled labor productivity by 2%, and unskilled labor productivity by 8%. For new exporters, skilled labor productivity rises by 45%, and unskilled labor productivity increases by 75% within four years of entering export markets.
Conference Presentations:
2024: SEA (Washington, DC)*
2023: V International Scientific Conference of Economics and Management Researchers (Baku, AZ)
(* scheduled)
Joint with Joonkyo Hong
In this study, we evaluate the reproducibility and replicability of Scott Orr’s (2022) innovative approach for identifying within-plant productivity differences across product lines. Orr’s methodology allows the estimation of plant-product level productivity, contingent upon a well-behaved pre-estimated demand system, which requires the use of carefully chosen instrumental variables (IVs) for output prices. Using Orr’s STATA replication package, we successfully replicate all primary estimates with the ASI Indian plant-level panel data from 2000 to 2007. Additionally, applying Orr’s replication codes to a sample from 2011 to 2020 reveals that the suggested IVs do not perform as expected.
Work in Progress
A Tale of Power and Progress: Productivity, Markups, and Markdowns in India's Automotive