We develop a quantitative spatial equilibrium model with endogenous migration and remittance decisions within households to examine the joint effect of migration and remittances on economic development. We apply the model to internal migration in China. Counterfactual analysis of the calibrated model shows that the presence of remittances increases migration and welfare, reduces regional inequality and facilitates structural change. Compared to a conventional single-person migration model, our household model suggests a larger reduction in regional inequality and stronger reallocation of employment from agriculture to manufacturing and services in response to the decline in migration costs over the period of 2000 to 2010.
- Ph.D., Princeton University
- B.A., Tsinghua University
Chang obtained his bachelor’s degree from Tsinghua University, and a Ph.D. in economics from Princeton University. He joined the HKU Business School in August, 2017. His research concerns the impact of globalization, especially the patterns and consequences of multinational production.
- International Trade
- Multinational Production
- Politics of Economic Reform in China (POLI3031)
- International Business Environment (STRA3702)
- “Internal Migration, Remittances And Economic Development,” (with Xiameng Pan), Journal of International Economics, forthcoming.
- “Uncertainty, imperfect information, and expectation formation over the firm’s life cycle,” (with Cheng Chen, Tatsuro Senga and Hongyong Zhang), Journal of Monetary Economics, forthcoming.
- “The economic costs of trade sanctions: Evidence from North Korea,” (with Jihee Kim, Kyoochul Kim and Sangyoon Park), Journal of International Economics, 2023, 145, 103813.
- “Learning and Information Transmission within Multinational Corporations,” (with Cheng Chen and Hongyong Zhang), European Economic Review, 2022, 143, 104016.
- “Multinational Production with Non-neutral Technologies,” Journal of International Economics, 2020, 123, 103294.
This paper investigates the economic costs of the recent United Nations sanctions on North Korea. Exploiting a novel data set on North Korean firms, we construct measures of regional exposure to export and intermediate input sanctions and show that trade sanctions cause sharp declines in local nighttime luminosity. Additional analysis of newly available product-level price data reveals that import sanctions led to significant increases in market prices. We then estimate a quantitative spatial equilibrium model using cross-region variations. The model implies that the sanctions reduced the country's manufacturing output by 12.9% and real income by 15.3%. We further quantify the potential impact of alternative sanction scenarios.
Using a long panel data set on Japanese firms, we find that firms make more precise forecasts and less autocorrelated forecast errors as they gain more experience. Then, we build a firm dynamics model where firms gradually learn about their demand by using a noisy signal. Using expectations data over time, we cleanly isolate the learning mechanism from other mechanisms and find that it accounts for 20%–40% of the overall decline in forecast errors over the life cycle. Productivity gains from removing information frictions range from 3% to 12%, with firm entry and exit playing prominent roles.
This paper develops a quantitative model of multinational production (MP) with non-neutral technologies incorporating two stylized facts observed in a global firm-level data: first, larger firms on average use more capital-intensive technologies; second, among firms producing in the same industry and country, those from more capital-abundant home countries use more capital-intensive technologies. I quantify the model using both firm-level and aggregate moments for 37 countries. I found that the reduction in MP costs accounts for 56% of the average decline in labor shares from 1996 to 2011, and the model also replicates a negative relationship between the change in a country's labor share and the change in the foreign affiliates' output share as observed in the data.